Stanford Thermal Earth Model for the Conterminous United States

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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.
Export Citation to RIS
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 Apr 11, 2024

Submitted Mar 14, 2024

Organization

Stanford University

Contact

Mohammad Aljubran

Authors

Mohammad Aljubran

Stanford University

Roland Horne

Stanford University

DOE Project Details

Project Name Wellbore Fracture Imaging Using Inflow Detection Measurements

Project Lead Lauren Boyd

Project Number EE0007080

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