使用内插物理信息图神经网络的美国大陆热地球模型

IF 2.9 2区 地球科学 Q3 ENERGY & FUELS
Mohammad J. Aljubran, Roland N. Horne
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引用次数: 0

摘要

本研究介绍了一种基于物理信息图神经网络的数据驱动型空间插值算法,用于开发美国大陆热地球模型。通过同时预测地下温度、地表热流和岩石热导率,该模型被训练为近似满足傅立叶传导热传递定律。除了井底温度测量数据外,我们还将其他空间和物理量作为模型输入,如深度、地理坐标、海拔、沉积厚度、磁异常、重力异常、放射性元素伽马射线通量、地震、电导率以及与断层和火山的距离。每个网格单元的空间分辨率为 $$18 \ km^2$$,我们预测了地表热流以及深度为 $$0-7 \ km$$、间隔为 $$1 \ km$$的温度和岩石热导率。我们的模型显示,温度、地表热流和热导率的平均绝对误差分别为 $$6.4^\circ C$$、$$6.9 \ mW/m^2$ 和 $$0.04 \ W/m-K$$。这种对地球热过程的全面建模对于理解地下现象和开发天然地下资源至关重要。我们的地球热模型可在 https://stm.stanford.edu 网站上以网络应用程序的形式提供,也可在 https://arcg.is/nLzzT0 的 ArcGIS 中以特征图层的形式提供,还可在 https://gdr.openei.org/submissions/1592 的地热数据储存库中以表格形式提供数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Geothermal Energy
Geothermal Energy Earth 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.
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