Appendices for Geothermal Exploration Artificial Intelligence Report
The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports.
The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
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|
| contactPoint |
{
"fn": "Jim Moraga",
"@type": "vcard:Contact",
"hasEmail": "mailto:jmoraga@mines.edu"
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| dataQuality |
true
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| description | The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "DOE Geodatabases.zip",
"format": "zip",
"accessURL": "https://gdr.openei.org/files/1303/DOE_GDB.zip",
"mediaType": "application/zip",
"description": "Geodatabases for Brady, Desert Peak, and Salton Sea Geothermal Sites. Needs o be used with ArcGIS or other geodatabase compatible GIS software."
},
{
"@type": "dcat:Distribution",
"title": "Deformation Analysis for Brady.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/Deformation%20Analysis%20for%20Brady.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "The Deformation Analysis Report outlines methods and technology used for identifying land deformations caused by geothermal activity. The report also includes description of the algorithm and methodology used to analyze and monitor the Brady Hot Spring Geothermal Field's deformation."
},
{
"@type": "dcat:Distribution",
"title": "Geodatabase Design.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/Geodatabase%20Design.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "Geodatabase Design outline for the Geothermal Exploration AI. Databases made using this design include Brady Hot Springs, Desert Peak and Salton Sea. The design outlines the data organization within each database. This document correlates to the design of the "DOE Geodatabases.zip" file within this submission."
},
{
"@type": "dcat:Distribution",
"title": "Geophysical Analysis Results.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/Geophysical%20analysis%20results.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "Report on Geophysical analysis results for Brady Geothermal Field. Includes seismic mapping and fault line mapping."
},
{
"@type": "dcat:Distribution",
"title": "Land Surface Temperature Report.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/LST%20Report%2020201026.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "The Land Surface Temperature (LST) Report explains the reasoning behind using LST as an input to the Geothermal Exploration AI algorithm. Included in the document is data from test sites, methodology for analysis, and results for Brady, Desert Peak and Salton Sea geothermal sites."
},
{
"@type": "dcat:Distribution",
"title": "Mineral Marker Maps.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/Mineral%20Marker%20Maps.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "Maps displaying the results of the Mineral Markers analysis. Maps show anomalies product of hydrothermally altered minerals in the area of interest. "
},
{
"@type": "dcat:Distribution",
"title": "Mineral Markers Methodology.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/Mineral%20Markers%20Methodology%2020201029.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "Report with the Methodology used for using Mineral Markers layer in the Geothermal AI. Applies to Brady and Desert Peak Geothermal Areas. Includes information regarding hyperspectral imaging and the processing of that data."
},
{
"@type": "dcat:Distribution",
"title": "Mineral Markers References Zotero Format.zip",
"format": "zip",
"accessURL": "https://gdr.openei.org/files/1303/MineralMarkers_Zotero_20200108.zip",
"mediaType": "application/zip",
"description": "Mineral Markers literature references in the Zotero format. Within the zip there are Zotero cache files along with links to each literature reference."
},
{
"@type": "dcat:Distribution",
"title": "Mineral Mapping Literature Report.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/Mineral_Mapping_lit.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "The Mineral Mapping Literature Report outlines the methods for remote sensing of geothermal sites and the application of these remote sensing methods. Methods include indicators that point to locations of geothermal sites. The report also summarizes the study areas which include the Salton Sea, Brady's Hot Spring, and Desert Peak."
},
{
"@type": "dcat:Distribution",
"title": "Morphology Literature.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/Morphology%20Litearture.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "Morphology Literature Report for Brady Geothermal Field. This report includes information regarding the use of morphological features as geothermal site indicators for the Geothermal Exploration Artificial Intelligence."
},
{
"@type": "dcat:Distribution",
"title": "Support Vector Machine Methodology.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/SVM%20Methodology%2020201026.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "Support Vector Machine (SVM) applied to the geothermal exploration report. This report explains why SVM was used with the Geothermal Exploration Artificial Intelligence. Includes information about the methodology of SVM analysis and the data from SVM analysis."
},
{
"@type": "dcat:Distribution",
"title": "Well Fault and Seismic Borders Report.docx",
"format": "docx",
"accessURL": "https://gdr.openei.org/files/1303/Wells_Fault_Seismic_Borders%20Report%2020201028.docx",
"mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"description": "Report about the borders for well, fault, and seismic data. Fault density data was used to define analysis boundaries. The report includes fault data for the three sites (Brady, Desert Peak, and Salton Sea) and reasoning for using fault data for the Geothermal Exploration Artificial Intelligence."
}
]
|
| DOI | 10.15121/1797280 |
| identifier | https://data.openei.org/submissions/7421 |
| issued | 2021-01-08T07:00:00Z |
| keyword |
[
"AI",
"ArcGis",
"Brady",
"California",
"Desert Peak",
"EGS",
"GIS",
"InSAR",
"Morphological",
"Morphology",
"Nevada",
"Python",
"SVM",
"SWIR",
"Salton Sea",
"TIR",
"VNIR",
"Zotero",
"anomaly detection",
"artificial intelligence",
"blind",
"blind system",
"border",
"code",
"conceptual model",
"database",
"deep learning",
"deformation",
"energy",
"engineered geothermal system",
"enhanced geothermal system",
"exploration",
"fault",
"geodatabase",
"geophysical",
"geophysics",
"geospatial data",
"geothermal",
"hydrothermal",
"hydrothermally altered minerals",
"hyperspectral",
"hyperspectral imaging",
"land surface temperature",
"machine learning",
"mineral markers",
"model",
"morphological features",
"preproccessed",
"processed data",
"radar",
"raw data",
"remote sensing",
"seismic",
"short wavelength infrared",
"site detection",
"support vector machine",
"thermal infrared",
"visible near infrared",
"well"
]
|
| landingPage | https://gdr.openei.org/submissions/1303 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2022-01-13T15:25:08Z |
| programCode |
[
"019:006"
]
|
| projectLead | Mike Weathers |
| projectNumber | EE0008760 |
| projectTitle | Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning |
| publisher |
{
"name": "Colorado School of Mines",
"@type": "org:Organization"
}
|
| spatial |
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|
| title | Appendices for Geothermal Exploration Artificial Intelligence Report |
