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..
Citation Formats
TY - DATA
AB - 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..
AU - Aljubran, Mohammad
A2 - Horne, Roland
DB - Geothermal Data Repository
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/2324793
KW - Thermal Earth Model
KW - Temperature
KW - geothermal
KW - energy
KW - Stanford
KW - temperature-at-depth
KW - heat flow
KW - rock thermal conductivity
KW - InterPIGNN
KW - physics-informed
KW - graph neural networks
KW - machine learning
KW - model
KW - temperature model
KW - ArcGIS
KW - API
KW - model inputs
KW - model outputs
KW - data-driven
KW - spatial interpolation
KW - algorithm
KW - heat conduction
KW - bottomhole temperature
LA - English
DA - 2024/03/14
PY - 2024
PB - Stanford University
T1 - Stanford Thermal Earth Model for the Conterminous United States
UR - https://doi.org/10.15121/2324793
ER -
Aljubran, Mohammad, and Roland Horne. Stanford Thermal Earth Model for the Conterminous United States. Stanford University, 14 March, 2024, Geothermal Data Repository. https://doi.org/10.15121/2324793.
Aljubran, M., & Horne, R. (2024). Stanford Thermal Earth Model for the Conterminous United States. [Data set]. Geothermal Data Repository. Stanford University. https://doi.org/10.15121/2324793
Aljubran, Mohammad and Roland Horne. Stanford Thermal Earth Model for the Conterminous United States. Stanford University, March, 14, 2024. Distributed by Geothermal Data Repository. https://doi.org/10.15121/2324793
@misc{GDR_Dataset_1592,
title = {Stanford Thermal Earth Model for the Conterminous United States},
author = {Aljubran, Mohammad and Horne, Roland},
abstractNote = {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..
},
url = {https://gdr.openei.org/submissions/1592},
year = {2024},
howpublished = {Geothermal Data Repository, Stanford University, https://doi.org/10.15121/2324793},
note = {Accessed: 2025-04-24},
doi = {10.15121/2324793}
}
https://dx.doi.org/10.15121/2324793
Details
Data from Mar 14, 2024
Last updated Jul 16, 2024
Submitted Mar 14, 2024
Organization
Stanford University
Contact
Mohammad Aljubran
Authors
Keywords
Thermal Earth Model, Temperature, geothermal, energy, Stanford, temperature-at-depth, heat flow, rock thermal conductivity, InterPIGNN, physics-informed, graph neural networks, machine learning, model, temperature model, ArcGIS, API, model inputs, model outputs, data-driven, spatial interpolation, algorithm, heat conduction, bottomhole temperatureDOE Project Details
Project Name Wellbore Fracture Imaging Using Inflow Detection Measurements
Project Lead Lauren Boyd
Project Number EE0007080