Stanford Thermal Earth Model for the Conterminous United States

Aljubran, Mohammad; Horne, Roland

doi:10.15121/2324793

Title: Stanford Thermal Earth Model for the Conterminous United States

Dataset
Other Related Research

Abstract

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..

Authors:

;

Stanford University

Publication Date:: Thu Mar 14 04:00:00 UTC 2024

Other Number(s):: 1592

DOE Contract Number:: EE0007080

Research Org.:: DOE Geothermal Data Repository; Stanford University

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)

Collaborations:: Stanford University

Subject:: 15 GEOTHERMAL ENERGY; 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

OSTI Identifier:: 2324793

DOI:: https://doi.org/10.15121/2324793

Citation Formats


                    Aljubran, Mohammad, and Horne, Roland. Stanford Thermal Earth Model for the Conterminous United States.  United States: N. p., 2024. 
        Web.  doi:10.15121/2324793.

Copy to clipboard


                    Aljubran, Mohammad, & Horne, Roland. Stanford Thermal Earth Model for the Conterminous United States.  United States.  doi:https://doi.org/10.15121/2324793

Copy to clipboard


                    Aljubran, Mohammad, and Horne, Roland. 2024.  
"Stanford Thermal Earth Model for the Conterminous United States".  United States.  doi:https://doi.org/10.15121/2324793.  https://www.osti.gov/servlets/purl/2324793. Pub date:Thu Mar 14 04:00:00 UTC 2024

Copy to clipboard


                    
@article{osti_2324793,

  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..},

  doi          = {10.15121/2324793},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu Mar 14 04:00:00 UTC 2024},

  month        = {Thu Mar 14 04:00:00 UTC 2024}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.15121/2324793

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States

Dataset Gomez-Velez, Jesus ; Siddik, Md Abu Bakar ; Turner, Sean ; ...

This dataset provides inputs, evaluation results, and trained weights from a large-sample Long Short-Term Memory (LSTM) model designed to predict daily stream temperatures across unregulated river reaches in the conterminous United States (CONUS). It includes dynamic meteorological and hydrologic forcings, static physiographic attributes, and model outputs from cross-validation experiments spanning 300 basins. It supports reproducible modeling, direct application for new basins, and provides data suitable for integration with reservoir and river simulations under current and future climates. It contains two .zip files described below · RQ-AI_runs.zip: Model outputs from 10-fold cross-validation experiments, including observed and predicted daily stream temperatures, alongmore »« less
Map of Pre-Operational Pumped Storage Hydropower in the Conterminous United States

Dataset Samu, Nicole
VEMAP - Phase I Database: An Integrated INPUT Dataset for ECOSYSTEM and Vegetation Modeling for the Conterminous United States

Dataset Kittel, T. G. F. ; Rosenbloom, N. A. ; Painter, T. H. ; ...

The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multi-institutional, international effort addressing the response of biogeography and biogeochemistry models to environmental variability in climate and other drivers in both space and time domains. The VEMAP input database for the Phase I model intercomparison includes compiled and model-generated datasets of long-term mean climate, soils, vegetation, and climate change scenarios for the conterminous United States. The data are on a 0.5 degree latitude/longitude grid. The data files include a VEMAP User's Guide, selected data and model output images, and README files under each data subdirectory to explain individual files. Updatesmore »« less
Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States

Dataset Rastogi, Deeksha ; Niu, Haoran ; Kao, Shih-Chieh

This dataset comprises high-resolution climate projections at 1/24 degree grid (~4km) over the conterminous United States (CONUS) based on ten Global Climate Models (GCMs) that are part of the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using two artificial intelligence (AI) techniques, primarily based on the computer vision approach called super-resolution. We train two separate networks: super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial network (SRGAN). The networks are trained using Daymet observations, originally available at a 1 km resolution. For training purposes, the Daymet data is interpolated to 1/24 degree (~4km), 0.25 degreemore »« less
HydroBio: Hydropower Capacity and Freshwater Biodiversity in Conterminous United States Sub-basins

Dataset Bozeman, Bryan ; Matson, Paul ; DeRolph, Chris ; ...

This dataset summarizes existing and potential hydropower capacity and freshwater biodiversity at the sub-basin level throughout the conterminous United States (CONUS). It contains descriptive information regarding each sub-basin (e.g., 8-digit hydrologic unit code identifier, name, states, and size) along with sub-basin-level summaries of: 1) existing hydropower capacity (MW), 2) potential nominal non-powered dam (NPD) capacity (MW), 3) potential capacity of new stream reach development (NSD) (MW), and 4) freshwater biodiversity, including the total richness of fish, crayfish, and mussels and metrics that account for how rare and threatened those species tend to be. Hydropower data were obtained from Oak Ridgemore »« less

Similar Records