Appalachian Basin Play Fairway Analysis: Thermal Quality Analysis in Low-Temperature Geothermal Play Fairway Analysis (GPFA-AB)

Published by Cornell University | Department of Energy | Metadata Last Checked: January 27, 2026 | Last Modified: 2021-07-07T19:25:46Z

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS). This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations. Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015. Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted. UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below)

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@type	dcat:Dataset
accessLevel	public
bureauCode	[ "019:20" ]
contactPoint	{ "fn": "Teresa E. Jordan", "@type": "vcard:Contact", "hasEmail": "mailto:tej1@cornell.edu" }
dataQuality	true
description	This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS). This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations. Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015. Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted. UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below)
distribution	[ { "@type": "dcat:Distribution", "title": "ArcGISToolbox_RegionalGridModels.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ArcGISToolbox_RegionalGridModels.zip", "mediaType": "application/zip", "description": "ArcGIS Toolbox used in Thermal Quality Analysis task and for the Risk Factor Analysis task of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. The Toolbox contains six different Regional Grid Models and one associated R Script. There is a Thermal Model, a Reservoir Productivity Model, three Risk of Seismicity Models, and 1 Utilization of Heat Model. The R script is for use with the Seismicity data. The Risk of Seismicity was estimated using two different methods (earthquake data and stress angle), as well as combined, hence the additional models. There is an associated file containing the Regional Grid Shapefiles and Raster (RegionalGridShapefilesAndRaster.zip). The shapefiles, ArcGIS toolbox, and R script contained within these two .zip files were used to convert vector and raster files to the standardized 1 square km grid used in this project. The code is general enough to be used in other studies that may need to work on a standard grid. ArcGIS 10.1 or later is needed to use the models in the toolbox. Details regarding methods for seismic risk factor conversion (within the toolbox) may be found in the memo contained within the project final report entitled 14_GPFA-AB_SeismicRiskMapCreationMethods.pdf (Smith and Horowitz, 2015). The R script AddNewSeisFieldsFunctions.R implements some of the methods described in the memo. Details about all of the ArcGIS toolbox models may be found in the memo entitled 16_GPFA-AB_RiskAnalysisAndRiskFactorDescriptions.pdf (Whealton, et al., 2015). Some models have been given different names since the memo was written. These models have the former names listed next to the current model name in the list above. " }, { "@type": "dcat:Distribution", "title": "CrossSections.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/CrossSections.zip", "mediaType": "application/zip", "description": "This is one of three associated .zip files relating to 'favorable counties results' within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This .zip file contains the Cross Section points and lines shapefiles as well as plots of results for depth to 80 degrees C and the thermal play fairway metric (PFM) on a 0 to 5 point scale. Image files (.png) are included. The cross validation results as image files (.png) for the predicted depth to 80 degrees C are included for each of the 5 cross section lines as well. The three associated .zip files contain raster for thermal resource predicted mean, standard error of predicted mean, and cross validation results on the county level for the 5 county maps made in the GPFA-AB project. Thermal resource cross sections made using the models in ThermalRiskFactorModels toolbox are also provided. Cross validation results are only shown for the Depth to 80 deg C variable, but other variables can be made using the provided functions and cross validation data. Details about the selected of the favorable counties are provided in 10_GPFA-AB_SelectBestThermalResourcesCounties.pdf (Smith, 2015), contained in the final report. The favorable counties referenced here were selected on the basis of the thermal quality analysis portion of the project. Four counties were selected from each of the three states in the study area (New York, Pennsylvania and West Virginia), for a total of twelve. Because some counties are adjacent, there are 5 county level maps. The image MapOfBestCounties.png within the collection of images shows all twelve highlighted on one map. • ChauErie refers to Erie County, PA and Chautauqua County, NY. • FayettePreston refers to Fayette County, PA and Preston county, WV • Gilmer refers to Gilmer County, WV • KnawLinc refers to Kanawha County, WV and Lincoln County, WV • FingerLakes refers to Stuben, Tomkins, and Chemung Counties of NY and Potter and Tiaga Counties of PA " }, { "@type": "dcat:Distribution", "title": "ImagesOfResultsFromCountyMapMakingModel.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ImagesOfResultsFromCountyMapMakingModel.zip", "mediaType": "application/zip", "description": "This is one of three associated .zip files relating to 'favorable counties results' within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This .zip file contains a series of map images (.png) of the 12 counties selected as the most favorable in terms of Thermal Quality (4 best for each of 3 states). Because several counties are adjacent, they may be shown with 5 county level maps. There are 21 images. There are 5 county level maps for predicted depth to 80 degrees C (ending in p), 5 county level maps showing the error associated with the predicted depth to 80 degrees C (ending in e), 5 county level maps showing Thermal Play Fairway Metric on a 5 color scale (where green is favorable), and 5 county level maps showing the standard deviation error associated with the Thermal Play Fairway Metric. In addition, there is an image titled 'MapOfBestCounties.png' that displays all 12 counties. The three associated .zip files contain raster for thermal resource predicted mean, standard error of predicted mean, and cross validation results on the county level for the 5 county maps made in the GPFA-AB project. Thermal resource cross sections made using the models in ThermalRiskFactorModels toolbox are also provided. Cross validation results are only shown for the Depth to 80 deg C variable, but other variables can be made using the provided functions and cross validation data. Details about the selected of the favorable counties are provided in 10_GPFA-AB_SelectBestThermalResourcesCounties.pdf (Smith, 2015), contained in the final report. The favorable counties referenced here were selected on the basis of the thermal quality analysis portion of the project. Four counties were selected from each of the three states in the study area (New York, Pennsylvania and West Virginia), for a total of twelve. Because some counties are adjacent, there are 5 county level maps. The image MapOfBestCounties.png within the collection of images shows all twelve highlighted on one map. • ChauErie refers to Erie County, PA and Chautauqua County, NY. • FayettePreston refers to Fayette County, PA and Preston county, WV • Gilmer refers to Gilmer County, WV • KnawLinc refers to Kanawha County, WV and Lincoln County, WV • FingerLakes refers to Stuben, Tomkins, and Chemung Counties of NY and Potter and Tiaga Counties of PA " }, { "@type": "dcat:Distribution", "title": "RScriptsForSortingDataAndRemovingOutliers.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/RScriptsForSortingDataAndRemovingOutliers.zip", "mediaType": "application/zip", "description": "This is one of seven .zip files associated files relating to thermal outlier assessment within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the R script mentioned below in the summary of the seven associated files. The seven files contain the well data sorted for outliers, and the R scripts used to process the data. Before running the outlier identification, wells in AASG_Thermed.xlsx (may be found within ThermalQualityAnalysisThermalModelDataFilesStateWellTemperatureDatabases.zip) were checked for the same spatial location. Only the deepest well in a given location is used for quality purposes. The R script SortingWells.R contains two functions that were developed to sort the data according to these specifications. Further details about the data processing are provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), provided within the project final report submitted on 10/16/2015. Outlier identification is done using the local identification function in Whealton and Smith (2015) with a 32 km searching radius for points. The nearest 25 points are used to check for outliers. Details about the algorithm are provided in 6_GPFA-AB_ThermalOutlierAssessment.pdf (Whealton and Stedinger, 2015), again within the final report. For ease in setting up the data for outlier identification, a function was written in R script DataArrangementAndRunOutlierAnalysis.R. This function sets up the data and runs the outlier identification function. " }, { "@type": "dcat:Distribution", "title": "RegionalGridShapefilesAndRaster.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/RegionalGridShapefilesAndRaster%20%281%29.zip", "mediaType": "application/zip", "description": "Regional Grid Shapefiles and Raster used in Thermal Quality Analysis task of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. Polygon (Fishnet2.shp and associated files), Point (Fishnet2_label.shp and associated files) and Raster grid (GridNAD.tif) are included, made using ArcGIS Create Fishnet tool. There is an associated file containing the ArcGIS Toolbox with the Regional Grid Models, (ArcGISToolbox_RegionalGridModels.zip) . The shapefiles, ArcGIS toolbox, and R script contained within these two .zip files were used to convert vector and raster files to the standardized 1 square km grid used in this project. The code is general enough to be used in other studies that may need to work on a standard grid. ArcGIS 10.1 or later is needed to use the models in the toolbox. Details regarding methods for seismic risk factor conversion (within the toolbox) may be found in the memo contained within the project final report entitled 14_GPFA-AB_SeismicRiskMapCreationMethods.pdf (Smith and Horowitz, 2015). The R script AddNewSeisFieldsFunctions. R implements some of the methods described in the memo. Details about all of the ArcGIS toolbox models may be found in the memo entitled 16_GPFA-AB_RiskAnalysisAndRiskFactorDescriptions.pdf (Whealton, et al., 2015). Some models have been given different names since the memo was written. These models have the former names listed next to the current model name in the list above. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisBHTCorrectionSections.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisBHTCorrectionSections.zip", "mediaType": "application/zip", "description": "This folder contains the shapefiles, SQL Query, and spreadsheets (.csv) of data associated with the BHT corrections for the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. The BHT correction sections were defined based on structural features within the Appalachian Basin, and geographic boundaries. The Rome Trough (e.g. Repetski et al. [2008]) defines the boundary between the Allegheny Plateau Correction section and the Zero Correction section. The Zero Correction section is for everywhere southeast of the Rome Trough in Pennsylvania, and is also used for Maryland because the well cluster in Maryland appears to be consistent with the well cluster across the border in Pennsylvania. The Allegheny Plateau is used for all wells located northwest of the Rome Trough. West Virginia, Virginia, and Kentucky all have a separate correction section defined based on wells in West Virginia. Further details about the use of BHT correction sections are provided in the file entitled 2_GPFA-AB_BHTCorrections.pdf (Whealton, Stedinger, and Horowitz, 2015), contained within the project final report. An image of the Rome Trough is provided in Repetski et al. (2008). This was georeferenced in ArcGIS based on county boundaries, and digitized using the Editor toolbar into Rome Trough final.shp. BHT_Corr_Sections.shp is the shapefile of the BHT correction sections. The attribute table contains a number from 0 to 3, indicating the region. This is the "reg" field in the BHT correction code in the Whealton (2015) code repository. WhealtonDrillingFluid.csv contains wells with drilling fluid as found by Whealton (2015). DrillingFluidMatches.csv are the wells that matched using the DrillingFluidQuery in Postgres Admin III. Further details about these processing steps are provided in entitled 8_GPFA-AB_ThermalModelMethods.pdf (Smith and Horowitz, 2015), contained within the project final report. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisFavorableCountiesThermalRiskFacotorModelsArcGISToolbox.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisFavorableCountiesThermalRiskFacotorModelsArcGISToolbox.zip", "mediaType": "application/zip", "description": "This is one of three associated .zip files relating to 'favorable counties results' within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This .zip file contains the ArcGIS Toolbox ThermalRiskFactorModels for clipping data to the counties of interest 'CountyMapMaking'. The three associated .zip files contain raster for thermal resource predicted mean, standard error of predicted mean, and cross validation results on the county level for the 5 county maps made in the GPFA-AB project. Thermal resource cross sections made using the models in ThermalRiskFactorModels toolbox are also provided. Cross validation results are only shown for the Depth to 80 deg C variable, but other variables can be made using the provided functions and cross validation data. Details about the selected of the favorable counties are provided in 10_GPFA-AB_SelectBestThermalResourcesCounties.pdf (Smith, 2015), contained in the final report. The favorable counties referenced here were selected on the basis of the thermal quality analysis portion of the project. Four counties were selected from each of the three states in the study area (New York, Pennsylvania and West Virginia), for a total of twelve. Because some counties are adjacent, there are 5 county level maps. The image MapOfBestCounties.png within the collection of images shows all twelve highlighted on one map. • ChauErie refers to Erie County, PA and Chautauqua County, NY. • FayettePreston refers to Fayette County, PA and Preston county, WV • Gilmer refers to Gilmer County, WV • KnawLinc refers to Kanawha County, WV and Lincoln County, WV • FingerLakes refers to Stuben, Tomkins, and Chemung Counties of NY and Potter and Tiaga Counties of PA " }, { "@type": "dcat:Distribution", "title": "AppalachianBasinGeothermalPlayFairways.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisPlayFairways.zip", "mediaType": "application/zip", "description": "This .zip file contains the Geothermal Play Fairways as identified by the GPFA-AB Team. Details about how these play fairways were selected are provided in: Jordan, T.E.. Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin: Final Phase 1 Research Report, U.S. Dept. of Energy Award No. DE-EE0006726. Principal Investigator Teresa Jordan. Submitted Oct. 16, 2015. The inner (high priority) and outer (medium priority) fairways are provided, along with ArcGIS symbology layers." }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalModelDataFilesCOSUNAColumnsAndSections.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalModelDataFilesCOSUNAColumnsAndSections%20%281%29.zip", "mediaType": "application/zip", "description": "This .zip file contains shapefiles and spreadsheets of data that were made from the AAPG (1985a; 1985b) COSUNA columns. Details about the processing of the data can be found in 4_GPFA-AB_ThermalConductivityStratigraphyCOSUNA (Smith, Jordan, and Frone, 2015) and 3_GPFA-AB_AnadarkoBasinThermalConductivity (Frone, 2015). All_COSUNA_Sections.shp contains polygons of the COSUNA sections within the Appalachian Basin. Field descriptions are contained within Smith, Jordan, and Frone (2015). COSUNA_Columns_NY-PA-WV-VA-OH-KY-MD.xlsm contains the stratigraphic information for all of the COSUNA columns within the region of interest in the Appalachian Basin. Please consult Smith, Jordan, and Frone (2015) for field header descriptions. Carter Conductivities.xlsx is the excel file of thermal conductivities used for general lithologies. Both Smith, Jordan, and Frone (2015), and Frone (2015) explain the selection of this data. The three files called NY_Conductivity_Final.xlsx, PA_Conductivity_Final.xlsx, and WV_Conductivity_Final.xlsx contain the results of the Monte Carlo Analysis of each formation to determine the mean and standard error of the thermal conductivity. The Monte Carlo Analysis methods are described in Smith, Jordan, and Frone (2015). (The file Conductivities.xlsx is simply a linked data table used in this analysis and is otherwise duplicative of Carter Conductivities.xlsx.)" }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalModelDataFilesSedimentThickness_dem30mallwgs.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalModelDataFilesSedimentThickness_dem30mallwgs.zip", "mediaType": "application/zip", "description": "This collection of 16 files is one of two .zip files associated with Sediment Thickness component of the Thermal Modeling within the Thermal Quality Analysis task of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. The other is ThermalQualityAnalysisThermalModelDataFilesSedimentThickness_tbrsedthickm.zip. The sediment thickness shapefile was made using contours of the Precambrian Basement from the Trenton Black River Project (WVGES, 2006). To obtain depth to basement, the elevation had to be added on to the depth to basement from sea level. The digital elevation model (DEM) used in this project is 30 m resolution from the USGS (2014). Latitudes from 37 deg N to 41deg N, and longitudes from 79 deg W to 85 deg W were used and mosaicked together using ArcGIS mosaic tool. Further details about the processing steps are provided in the file entitled 8_GPFA-AB_ThermalModelMethods.pdf (Smith and Horowitz, 2015), provided within the project final report." }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalModelDataFilesSedimentThickness_tbrsedthickm.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalModelDataFilesSedimentThickness_tbrsedthickm.zip", "mediaType": "application/zip", "description": "This is one of two .zip files associated with Sediment Thickness component of the Thermal Modeling within the Thermal Quality Analysis task of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. The other is ThermalQualityAnalysisThermalModelDataFilesSedimentThickness_dem30mallwgs.zip. The sediment thickness shapefile was made using contours of the Precambrian Basement from the Trenton Black River Project (WVGES, 2006). To obtain depth to basement, the elevation had to be added on to the depth to basement from sea level. The digital elevation model (DEM) used in this project is 30 m resolution from the USGS (2014). Latitudes from 37deg N to 41deg N, and longitudes from 79deg W to 85deg W were used and mosaicked together using ArcGIS mosaic tool. Further details about the processing steps are provided in the file entitled 8_GPFA-AB_ThermalModelMethods.pdf (Smith and Horowitz, 2015), provided within the project final report. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalModelDataFilesStateWellTemperatureDatabases.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalModelDataFilesStateWellTemperatureDatabases.zip", "mediaType": "application/zip", "description": "This .zip file contains 5 Microsoft Excel workbooks, one with macros enabled: All_States_BHT_HeatFlow_Raw_Combined.xlsm, AASG_Combined.xlsx, AASG_Processed.xlsx, AASG_Thermed.xlsx, and AASG_Thermed_AllThicksAndConds.xlsx. The raw data in these databases was pulled from the American Association of State Geologists (AASG), in association with the National Geothermal Data System (NGDS). A unique identifier called StateID is common in all of these databases, and it may be used to join the databases together, and also back to the original data (the Raw file). These data have been processed and interpreted as part of the Thermal Quality Analysis task. Processing details, and file and field name definitions for these databases may be found 8_GPFA-AB_ThermalModelMethods.pdf (Smith and Horowitz, 2015), which was provided within the final report for this project. The only difference between the database entitled AASG_Thermed.xlsx and AASG_Thermed_AllThicksAndConds.xlsx is that the latter has columns for the formation thicknesses (Layer) and thermal conductivities (Cond) corresponding to each thickness for each well. There are also columns for the temperature at 10 m (T0) to 5000 m (T499) in 10 m increments. Note that some records in AASG_Thermed.xlsx and AASG_Thermed_AllThicksAndConds.xlsx have -9999 in the Depth50C, Depth80C, and Depth100C. This is because the maximum depth for these fields was 20000 m, and for these records one would have to go deeper than 20000 m to reach 50 deg C, 80 deg C, or 100 deg C. The calculation of the geotherm and surface heat flow for these wells was done using the code repository developed by Smith, Horowitz, and Whealton (2015) and described in 8_GPFA-AB_ThermalModelMethods.pdf (Smith and Horowitz, 2015). " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsArcGISToolbox.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsArcGISToolbox.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains an ArcGIS Toolbox with 6 ArcGIS Models: WellClipsToWormsSections, BufferedRasterToClippedRaster, ExtractThermalPropertiesToCrossSection, AddExtraInfoToCrossSection, and CrossSectionExtraction. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataCrossValidationResults.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataCrossValidationResults.zip", "mediaType": "application/zip", "description": "This is one of sixteen .associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the results of the cross validation using LeaveOneOutCrossValidation.R of the predicted values for surface heat flow, depth to 80 degrees C, depth to 100 degrees C, temperature at 1.5 km, temperature at 2.5 km and temperature at 3.5 km. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataDepth100cerr.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataDepth100cerr.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the error associated with the predicted depth to 100 degrees C. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataDepth100cpred.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataDepth100cpred.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the predicted depth to 100 degrees C. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataDepth80cerr.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataDepth80cerr.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the error associated with the predicted depth to 80 degrees C. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataDepth80cpred.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataDepth80cpred.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the predicted depth to 80 degrees C. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataFilesImages.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataFilesImages.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains 6 images (.png) including predicted and associated error for surface heat flow, depth to 80 degrees C, depth to 100 degrees C, temperature at 1.5 km, temperature at 2.5 km and temperature at 3.5 km. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataHeatFlowPred.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataHeatFlowPred.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the predicted surface heat flow. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataHeatflowErr.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataHeatflowErr.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the error associated with the predicted surface heat flow. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt1500mErr.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt1500mErr.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the error associated with the predicted temperature at 1.5km. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt1500mPred.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt1500mPred.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the predicted temperature at 1.5km. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt2500mErr.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt2500mErr.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the error associated with the predicted temperature at 2.5km. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt2500mPred.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt2500mPred.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the predicted temperature at 2.5km. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt3500mErr.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt3500mErr.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the error associated with the predicted temperature at 3.5km. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt3500mPred.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsDataTempAt3500mPred.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the predicted temperature at 3.5km. The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisThermalResourceInterpolationResultsRScriptsForInterpolation.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisThermalResourceInterpolationResultsRScriptsForInterpolation.zip", "mediaType": "application/zip", "description": "This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains two R scripts used for interpolation: StratifiedKrigingInterpolation.R and LeaveOneOutCrossValidation.R The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in "pred" are the predicted mean for that resource, and files ending in "err" are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource. Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report. The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource. A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource. Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWellDataOutliersRemovedPredDepthTo100C.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWellDataOutliersRemovedPredDepthTo100C.zip", "mediaType": "application/zip", "description": "This is one of seven .zip files associated files relating to thermal outlier assessment within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the data pertinent to the predicted depth to 100 degrees C. The seven files contain the well data sorted for outliers, and the R scripts used to process the data. Before running the outlier identification, wells in AASG_Thermed.xlsx (may be found within ThermalQualityAnalysisThermalModelDataFilesStateWellTemperatureDatabases.zip) were checked for the same spatial location. Only the deepest well in a given location is used for quality purposes. The R script SortingWells.R contains two functions that were developed to sort the data according to these specifications. Further details about the data processing are provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), provided within the project final report submitted on 10/16/2015. Outlier identification is done using the local identification function in Whealton and Smith (2015) with a 32 km searching radius for points. The nearest 25 points are used to check for outliers. Details about the algorithm are provided in 6_GPFA-AB_ThermalOutlierAssessment.pdf (Whealton and Stedinger, 2015), again within the final report. For ease in setting up the data for outlier identification, a function was written in R script DataArrangementAndRunOutlierAnalysis.R. This function sets up the data and runs the outlier identification function. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWellDataOutliersRemovedPredDepthTo80C.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWellDataOutliersRemovedPredDepthTo80C.zip", "mediaType": "application/zip", "description": "This is one of seven .zip files associated files relating to thermal outlier assessment within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the data pertinent to the predicted depth to 80 degrees C. The seven files contain the well data sorted for outliers, and the R scripts used to process the data. Before running the outlier identification, wells in AASG_Thermed.xlsx (may be found within ThermalQualityAnalysisThermalModelDataFilesStateWellTemperatureDatabases.zip) were checked for the same spatial location. Only the deepest well in a given location is used for quality purposes. The R script SortingWells.R contains two functions that were developed to sort the data according to these specifications. Further details about the data processing are provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), provided within the project final report submitted on 10/16/2015. Outlier identification is done using the local identification function in Whealton and Smith (2015) with a 32 km searching radius for points. The nearest 25 points are used to check for outliers. Details about the algorithm are provided in 6_GPFA-AB_ThermalOutlierAssessment.pdf (Whealton and Stedinger, 2015), again within the final report. For ease in setting up the data for outlier identification, a function was written in R script DataArrangementAndRunOutlierAnalysis.R. This function sets up the data and runs the outlier identification function. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWellDataOutliersRemovedPredTempAt1500m.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWellDataOutliersRemovedPredTempAt1500m.zip", "mediaType": "application/zip", "description": "This is one of seven .zip files associated files relating to thermal outlier assessment within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the data pertinent to the predicted depth at 1.5 km. The seven files contain the well data sorted for outliers, and the R scripts used to process the data. Before running the outlier identification, wells in AASG_Thermed.xlsx (may be found within ThermalQualityAnalysisThermalModelDataFilesStateWellTemperatureDatabases.zip) were checked for the same spatial location. Only the deepest well in a given location is used for quality purposes. The R script SortingWells.R contains two functions that were developed to sort the data according to these specifications. Further details about the data processing are provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), provided within the project final report submitted on 10/16/2015. Outlier identification is done using the local identification function in Whealton and Smith (2015) with a 32 km searching radius for points. The nearest 25 points are used to check for outliers. Details about the algorithm are provided in 6_GPFA-AB_ThermalOutlierAssessment.pdf (Whealton and Stedinger, 2015), again within the final report. For ease in setting up the data for outlier identification, a function was written in R script DataArrangementAndRunOutlierAnalysis.R. This function sets up the data and runs the outlier identification function. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWellDataOutliersRemovedPredTempAt2500m.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWellDataOutliersRemovedPredTempAt2500m.zip", "mediaType": "application/zip", "description": "This is one of seven .zip files associated files relating to thermal outlier assessment within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the data pertinent to the predicted depth at 2.5 km. The seven files contain the well data sorted for outliers, and the R scripts used to process the data. Before running the outlier identification, wells in AASG_Thermed.xlsx (may be found within ThermalQualityAnalysisThermalModelDataFilesStateWellTemperatureDatabases.zip) were checked for the same spatial location. Only the deepest well in a given location is used for quality purposes. The R script SortingWells.R contains two functions that were developed to sort the data according to these specifications. Further details about the data processing are provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), provided within the project final report submitted on 10/16/2015. Outlier identification is done using the local identification function in Whealton and Smith (2015) with a 32 km searching radius for points. The nearest 25 points are used to check for outliers. Details about the algorithm are provided in 6_GPFA-AB_ThermalOutlierAssessment.pdf (Whealton and Stedinger, 2015), again within the final report. For ease in setting up the data for outlier identification, a function was written in R script DataArrangementAndRunOutlierAnalysis.R. This function sets up the data and runs the outlier identification function. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWellDataOutliersRemovedPredTempAt3500m.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWellDataOutliersRemovedPredTempAt3500m.zip", "mediaType": "application/zip", "description": "This is one of seven .zip files associated files relating to thermal outlier assessment within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the data pertinent to the predicted depth at 3.5 km. The seven files contain the well data sorted for outliers, and the R scripts used to process the data. Before running the outlier identification, wells in AASG_Thermed.xlsx (may be found within ThermalQualityAnalysisThermalModelDataFilesStateWellTemperatureDatabases.zip) were checked for the same spatial location. Only the deepest well in a given location is used for quality purposes. The R script SortingWells.R contains two functions that were developed to sort the data according to these specifications. Further details about the data processing are provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), provided within the project final report submitted on 10/16/2015. Outlier identification is done using the local identification function in Whealton and Smith (2015) with a 32 km searching radius for points. The nearest 25 points are used to check for outliers. Details about the algorithm are provided in 6_GPFA-AB_ThermalOutlierAssessment.pdf (Whealton and Stedinger, 2015), again within the final report. For ease in setting up the data for outlier identification, a function was written in R script DataArrangementAndRunOutlierAnalysis.R. This function sets up the data and runs the outlier identification function. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWellDataOutliersRemovedSurfaceHeatFlow.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWellDataOutliersRemovedSurfaceHeatFlow.zip", "mediaType": "application/zip", "description": "This is one of seven .zip files associated files relating to thermal outlier assessment within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the data pertinent to the predicted surface heat flow. The seven files contain the well data sorted for outliers, and the R scripts used to process the data. Before running the outlier identification, wells in AASG_Thermed.xlsx (may be found within ThermalQualityAnalysisThermalModelDataFilesStateWellTemperatureDatabases.zip) were checked for the same spatial location. Only the deepest well in a given location is used for quality purposes. The R script SortingWells.R contains two functions that were developed to sort the data according to these specifications. Further details about the data processing are provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), provided within the project final report submitted on 10/16/2015. Outlier identification is done using the local identification function in Whealton and Smith (2015) with a 32 km searching radius for points. The nearest 25 points are used to check for outliers. Details about the algorithm are provided in 6_GPFA-AB_ThermalOutlierAssessment.pdf (Whealton and Stedinger, 2015), again within the final report. For ease in setting up the data for outlier identification, a function was written in R script DataArrangementAndRunOutlierAnalysis.R. This function sets up the data and runs the outlier identification function. " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridCNY.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridCNY.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for Central New York. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridCT.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridCT.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for Chautauqua County, New York. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridCWV.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridCWV.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for Central West Virginia. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridENY.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridENY.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for Eastern New York. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridENYPA.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridENYPA.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for Eastern New York and Eastern Pennsylvania. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridMT.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridMT.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for Western West Virginia. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridNWPANY.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridNWPANY.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for northwestern Pennsylvania and Northwestern New York. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridSWPA.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridSWPA.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for Southwestern Pennsylvania. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridVR.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridVR.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the Valley and Ridge. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolation1kmGridWPA.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolation1kmGridWPA.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for Western Pennsylvania. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolationRegions50kmBufferedInterpolationRegions.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolationRegions50kmBufferedInterpolationRegions.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the 50 km Buffered Interpolation Regions as a series of shapefiles (exterior buffer only). The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolationRegionsToolboxModelForBoundaries.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolationRegionsToolboxModelForBoundaries.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the model (called 'BoundaryCreationUsingWorms') that was used to create boundaries in ArcGIS Toolbox, "ThermalRiskFactorModels.tbx". The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "ThermalQualityAnalysisWormBasedInterpolationRegionsUnbufferedInterpolationRegions.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/ThermalQualityAnalysisWormBasedInterpolationRegionsUnbufferedInterpolationRegions.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains two image files (.png) and shapefiles of the UnBuffered Interpolation regions. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "WormData.zip", "format": "zip", "accessURL": "https://gdr.openei.org/files/638/WormData.zip", "mediaType": "application/zip", "description": "This is one of 14 associated files relating to Worm Based Interpolation Boundaries, as part of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains two shapefiles, used for display purposes (GravWorms_18+.shp and MagWorms_18+.shp), as mentioned below. The 14 files .zip files contain the data used and the products of the worm-based delineation of interpolation boundaries using ArcGIS. The gravity and pseudogravity (magnetic) worms are contained in two SQLite databases that are part of a separate data submission, "Risk of Seismicity in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)". These databases were loaded into QGIS and queried for only those worm levels (worm_level) that were greater than 18, which is essentially deeper than 18 km. These were converted into a shapefile for display purposes (GravWorms_18+.shp and MagWorms_18+.shp). Digitization of the interpolation boundaries was done in ArcGIS using the Editor toolbar, and was based primarily on the gravity worms. Only one region, SWPA, required information from the magnetic worms because of a strong magnetic contrast that defines this section. Names of the boundaries were made according to geographic location within the Appalachian Basin. For example, the so-called Valley and Ridge boundary roughly follows the Valley and Ridge physiographic province. Further details, including full names of the sections, are provided within the file called 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (within the project final report). Note that section MT and section WWV are used interchangeably to refer to the same section. Section names used in the file name schema: CNY = Central New York; CT = Chautauqua County, New York; CWV = Central West Virginia; ENY = Eastern New York; ENYPA = Eastern New York and Eastern Pennsylvania; MT = Western West Virginia; NWPANDY = Northwestern Pennsylvania and Northwestern New York; SWPA = Southwestern Pennsylvania; VR = Valley and Ridge; WPA = Western Pennsylvania " }, { "@type": "dcat:Distribution", "title": "Updated Thermal Quality Analysis", "format": "HTML", "accessURL": "https://gdr.openei.org/submissions/879", "mediaType": "text/html", "description": "Link to a newer GDR submission with improved thermal quality analysis " }, { "@type": "dcat:Distribution", "title": "Updated Combined Risk Factor Analysis", "format": "HTML", "accessURL": "https://gdr.openei.org/submissions/880", "mediaType": "text/html", "description": "Link to newer GDR submission with revised combined risk factor analysis" } ]
DOI	10.15121/1261947
identifier	https://data.openei.org/submissions/6875
issued	2015-11-15T07:00:00Z
keyword	[ "Appalachian basin", "ArcGIS", "BHT correction", "COSUNA", "Correlation of Stratigraphic Units of North America", "DEM", "Digital Elevation Model", "GPFA-AB", "Kriging", "New York", "PFA", "Pennsylvania", "R script", "Rome Trough", "Trenton Black River Project", "West Virginia", "basement", "cross validation", "cross-section", "deep direct use", "depth-to-temperature", "district heating", "favorable counties", "geospatial data", "geothermal", "geothermal play fairway analysis", "geotherms", "gravity", "heat flow", "low temperature", "low-temperature", "magnetics", "outlier", "play fairway analysis", "raster", "regional grid", "resource assessment", "risk of seismicity", "sediment thickness", "shapefile", "temperature-at-depth", "thermal analysis", "thermal conductivity", "thermal field", "thermal model", "thermal quality", "worm based interpolation boundaries", "worms" ]
landingPage	https://gdr.openei.org/submissions/638
license	https://creativecommons.org/licenses/by/4.0/
modified	2021-07-07T19:25:46Z
programCode	[ "019:006" ]
projectLead	Holly Thomas
projectNumber	EE0006726
projectTitle	Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin
publisher	{ "name": "Cornell University", "@type": "org:Organization" }
spatial	"{"type":"Polygon","coordinates":[[[-82.5,37],[-74.5,37],[-74.5,43.5],[-82.5,43.5],[-82.5,37]]]}"
title	Appalachian Basin Play Fairway Analysis: Thermal Quality Analysis in Low-Temperature Geothermal Play Fairway Analysis (GPFA-AB)