Stanford Thermal Earth Model for the Conterminous United States
Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below.
A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively..
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Mohammad Aljubran",
"@type": "vcard:Contact",
"hasEmail": "mailto:aljubrmj@stanford.edu"
}
|
| dataQuality |
true
|
| description | Provided here are various forms of the Stanford Thermal Earth Model, as well as the data and methods used for its creation. The predictions produced by this model were visualized in two-dimensional spatial maps across the modeled depths (0-7 km) for the conterminous United States. The thermal earth model is made available as an application programming interface (API) and as feature layers on ArcGIS, which are both provided via links below. A data-driven spatial interpolation algorithm based on physics-informed graph neural networks was used to develop these national temperature-at-depth maps. The model satisfied the three-dimensional heat conduction law by predicting subsurface temperature, surface heat flow, and rock thermal conductivity. Many physical quantities, including bottomhole temperature, depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity were used as model inputs. Surface heat flow, temperature, and thermal conductivity predictions were constructed for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km2 per grid cell. The model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8C, 8.1 mW/m2 and 0.07 W/(C-m), respectively.. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Thermal Earth Model Preprint",
"format": "09961",
"accessURL": "https://arxiv.org/abs/2403.09961",
"mediaType": "application/octet-stream",
"description": "This is a link to a preprint that covers the study in detail."
},
{
"@type": "dcat:Distribution",
"title": "Thermal Earth Model Journal Article",
"format": "HTML",
"accessURL": "https://doi.org/10.1186/s40517-024-00304-7",
"mediaType": "text/html",
"description": "This is a link to these data's associated journal article. The paper covers how an interpolative physics-informed graph neural network (InterPIGNN) was used to create the thermal earth model. The paper details input data collection, methods, and results from the project."
},
{
"@type": "dcat:Distribution",
"title": "Bottom Hole Temperature Data.csv",
"format": "csv",
"accessURL": "https://gdr.openei.org/files/1592/Raw_BHT_aggregated_data.csv",
"mediaType": "text/csv",
"description": "This CSV hosts all raw data that was aggregated for bottomhole temperature measurements. File column information includes: location, depth, state, source, and temperature."
},
{
"@type": "dcat:Distribution",
"title": "thermal_model_inputs_outputs_Ver2.csv",
"format": "csv",
"accessURL": "https://gdr.openei.org/files/1592/stanford_thermal_inputs_outputs_across_depths.csv",
"mediaType": "text/csv",
"description": "**Outdated - see thermal_model_inputs_outputs_Ver3.csv for more complete set of model inputs and outputs.** "
},
{
"@type": "dcat:Distribution",
"title": "thermal_model_inputs_outputs_Ver1.csv",
"format": "csv",
"accessURL": "https://gdr.openei.org/files/1592/stanford_thermal_model_inputs_outputs.csv",
"mediaType": "text/csv",
"description": "**Outdated - see thermal_model_inputs_outputs_Ver3.csv for more complete set of model inputs and outputs.**"
},
{
"@type": "dcat:Distribution",
"title": "thermal_model_inputs_outputs_Ver1.json",
"format": "json",
"accessURL": "https://gdr.openei.org/files/1592/stanford_thermal_model_inputs_outputs.json",
"mediaType": "application/octet-stream",
"description": "**Outdated - see thermal_model_inputs_outputs_Ver3.csv for more complete set of model inputs and outputs.** "
},
{
"@type": "dcat:Distribution",
"title": "thermal_model_inputs_outputs_Ver3.csv",
"format": "csv",
"accessURL": "https://gdr.openei.org/files/1592/stanford_thermal_model_inputs_outputs_COMPLETE_VERSION2.csv",
"mediaType": "text/csv",
"description": "**Most up-to-date version** This CSV file provides all updated model input/output quantities across depths of 1-7km for the contiguous United States. Parameters are explained in detail in the attached paper."
},
{
"@type": "dcat:Distribution",
"title": "Stanford Temperature Model - API",
"format": "edu",
"accessURL": "https://stm.stanford.edu/",
"mediaType": "application/octet-stream",
"description": "This API hosts an upscaled version of the temperature-at-depth model for the conterminous United States. Location search utilities (latitude [deg], longitude [deg], and depth [meters]) are provided for easily utilizing model predictions."
},
{
"@type": "dcat:Distribution",
"title": "Stanford Temperature Model - ArcGIS Layers",
"format": "HTML",
"accessURL": "https://www.arcgis.com/home/webmap/viewer.html?webmap=3b864b04fba44950863b103d78703ae3&extent=-137.6849,9.1413,-40.6537,56.0451",
"mediaType": "text/html",
"description": "This ArcGIS form of the model is provided via temperature prediction layers at various depths. Layers for temperature predictions exist for depths of 0-7km, at 1km increments. Layers for model inputs and outputs as well as surface heat flow predictions are also provided."
}
]
|
| DOI | 10.15121/2324793 |
| identifier | https://data.openei.org/submissions/7669 |
| issued | 2024-03-14T06:00:00Z |
| keyword |
[
"API",
"ArcGIS",
"InterPIGNN",
"Stanford",
"Temperature",
"Thermal Earth Model",
"algorithm",
"bottomhole temperature",
"data-driven",
"energy",
"geothermal",
"graph neural networks",
"heat conduction",
"heat flow",
"machine learning",
"model",
"model inputs",
"model outputs",
"physics-informed",
"rock thermal conductivity",
"spatial interpolation",
"temperature model",
"temperature-at-depth"
]
|
| landingPage | https://gdr.openei.org/submissions/1592 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2024-07-16T16:04:35Z |
| programCode |
[
"019:006"
]
|
| projectLead | Lauren Boyd |
| projectNumber | EE0007080 |
| projectTitle | Wellbore Fracture Imaging Using Inflow Detection Measurements |
| publisher |
{
"name": "Stanford University",
"@type": "org:Organization"
}
|
| spatial |
"{"type":"Polygon","coordinates":[[[-125.36348124999998,24.267251345221194],[-66.13628750000002,24.267251345221194],[-66.13628750000002,49.492575376927796],[-125.36348124999998,49.492575376927796],[-125.36348124999998,24.267251345221194]]]}"
|
| title | Stanford Thermal Earth Model for the Conterminous United States |
