Department of Energy
Dataset 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..
Organization: Department of Energy
Organization URL: https://catalog.data.gov/organization/energy
Last updated: 2024-07-16T16:04:35Z
Tags: 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