Thermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)

TL;DR

This paper introduces a physics-informed graph neural network model that accurately predicts subsurface temperature, heat flow, and thermal conductivity across the US, aiding geological understanding and resource exploration.

Contribution

The study develops a novel interpolative GNN model incorporating physical laws and diverse geophysical data for detailed thermal mapping of the Earth's subsurface.

Findings

Achieved mean absolute errors of 4.8°C for temperature, 5.817 mW/m² for heat flow, and 0.022 W/(C-m) for thermal conductivity.

Produced detailed 3D maps of subsurface thermal properties at 1 km intervals.

Demonstrated the model's superiority over traditional methods in spatial accuracy and physical consistency.

Abstract

This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop national temperature-at-depth maps for the conterminous United States. The model was trained to approximately satisfy the three-dimensional heat conduction law by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, and electric conductivity. We constructed surface heat flow, and temperature and thermal conductivity predictions for depths of 0-7 km at an interval of 1 km with spatial resolution of 18 km$^2$ per grid cell. Our model showed superior temperature, surface heat flow and thermal conductivity mean absolute errors of 4.8{\deg} C, 5.817 mW/m$^2$ and 0.022 W/(C-m)$, respectively. The predictions were visualized in two-dimensional spatial maps across the modeled depths. This thorough modeling of the Earth's thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources.