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
Stanford University. (2024). Stanford Thermal Earth Model for the Conterminous United States [data set]. Retrieved from https://dx.doi.org/10.15121/2324793.
Aljubran, Mohammad, Horne, Roland. Stanford Thermal Earth Model for the Conterminous United States. United States: N.p., 14 Mar, 2024. Web. doi: 10.15121/2324793.
Aljubran, Mohammad, Horne, Roland. Stanford Thermal Earth Model for the Conterminous United States. United States. https://dx.doi.org/10.15121/2324793
Aljubran, Mohammad, Horne, Roland. 2024. "Stanford Thermal Earth Model for the Conterminous United States". United States. https://dx.doi.org/10.15121/2324793. https://gdr.openei.org/submissions/1592.
@div{oedi_1592, title = {Stanford Thermal Earth Model for the Conterminous United States}, author = {Aljubran, Mohammad, 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..
}, doi = {10.15121/2324793}, url = {https://gdr.openei.org/submissions/1592}, journal = {}, number = , volume = , place = {United States}, year = {2024}, month = {03}}
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