{"title":"使用内插物理信息图神经网络的美国大陆热地球模型","authors":"Mohammad J. Aljubran, Roland N. Horne","doi":"10.1186/s40517-024-00304-7","DOIUrl":null,"url":null,"abstract":"<div><p>This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier’s Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of <span>\\(18 \\ km^2\\)</span> per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of <span>\\(0-7 \\ km\\)</span> at an interval of <span>\\(1 \\ km\\)</span>. Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of <span>\\(6.4^\\circ C\\)</span>, <span>\\(6.9 \\ mW/m^2\\)</span> and <span>\\(0.04 \\ W/m-K\\)</span>, respectively. This thorough modeling of the Earth’s thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. 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Thermal Earth model for the conterminous United States using an interpolative physics-informed graph neural network
This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier’s Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of \(18 \ km^2\) per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of \(0-7 \ km\) at an interval of \(1 \ km\). Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of \(6.4^\circ C\), \(6.9 \ mW/m^2\) and \(0.04 \ W/m-K\), respectively. This thorough modeling of the Earth’s thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. Our thermal Earth model is available as web application at https://stm.stanford.edu, feature layers on ArcGIS at https://arcg.is/nLzzT0, and tabulated data on the Geothermal Data Repository at https://gdr.openei.org/submissions/1592.
Geothermal EnergyEarth and Planetary Sciences-Geotechnical Engineering and Engineering Geology
CiteScore
5.90
自引率
7.10%
发文量
25
审稿时长
8 weeks
期刊介绍:
Geothermal Energy is a peer-reviewed fully open access journal published under the SpringerOpen brand. It focuses on fundamental and applied research needed to deploy technologies for developing and integrating geothermal energy as one key element in the future energy portfolio. Contributions include geological, geophysical, and geochemical studies; exploration of geothermal fields; reservoir characterization and modeling; development of productivity-enhancing methods; and approaches to achieve robust and economic plant operation. Geothermal Energy serves to examine the interaction of individual system components while taking the whole process into account, from the development of the reservoir to the economic provision of geothermal energy.