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| Machine learning-based calculation and characteristic analysis of terrestrial heat flow in the Songliao Basin |
GONG Ming-Xu1( ), BAI Li-Ge2, ZENG Zhao-Fa2(), WU Feng-Shou1 |
1. China Ship Survey and Design Institute Co.,Ltd.,Shanghai 200063,China
2. College of Geo-Exploration Science and Technology,Jilin University,Changchun 130026,China |
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Abstract Terrestrial heat flow has a high reference value for the evaluation of geothermal resources since it can directly indicate the Earth's internal heat on the surface.However,no high-quality and high-resolution terrestrial heat flow measurements have been conducted in the Songliao Basin due to costly and difficult conventional heat flow measurements based on the drilling technology.Machine learning,as a technology for data analysis,can identify patterns in data and utilize these patterns to automatically calculate unknown data.This study calculated the regional terrestrial heat flow using the machine learning method.Based on the measured data of global terrestrial heat flow and the geological structure data,both the Kriging regression algorithm and the machine learning algorithm were used to calculate the terrestrial heat flow in a known heat flow distribution area,as well as the root mean square error and the correlation coefficient.The machine learning algorithm yielded results with a smaller error and a higher correlation.Then,the terrestrial heat flow in the Songliao Basin was calculated using the machine learning method.As revealed by the calculation results,the terrestrial heat flow is the highest (more than 80 mWm-2) in the Songliao basin and gradually decreases outward in a circular pattern centered on the area between Daqing and Songyuan.The results are highly consistent with the measured results of the regional geothermal gradient,providing a reference for further analysis of the distribution patterns of geothermal resources in the Songliao Basin.Finally,the sensitivity of geological characteristics was analyzed using the Sobol method to quantify the effects of various parameters.This study verifies that the machine learning method has a high research and application value in the calculation of terrestrial heat flow.
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Received: 20 May 2022
Published: 05 July 2023
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Neural network classification
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| 变量名称 | 数据量 | 参考文献 | | 地表地形 | 64800 | Amante and Eakins(2009)[19] | | 莫霍面深度 | 64800 | Reguzzoni et al.(2013)[20];
刘卓(2019)[13] | | 岩石圈—软流圈边界 | 64800 | Pasyanos et al.(2014)[21] ;
刘子婧(2016)[14] | | 大陆岩石圈年龄 | 5365 | Poupinet and Shapiro(2009)[22] | | 布格重力异常 | 64800 | Balmino et al.(2012)[23] | | 地壳厚度 | 64800 | Laske et al.(2013)[24];
王开燕等(2015)[15] | | 上地幔密度异常 | 64800 | Kaban et al.(2004)[25] | | 磁异常 | 64800 | Maus et al.(2013)[26] | | 上、下地壳厚度 | 64800 | Bassin(2000)[27] | | 生热率 | 64800 | USGS(2016)[28] | | 上地幔速度结构 | 64800 | Shapiro and Ritzwoller(2002)[29] | | 岩石类型 | 64800 | Hartmann and Moosdorf(2012)[30] | | 到洋脊的距离 | 64800 | Coffin et al.(1998)[31]
(UTIG Plates Project) |
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List of global parameter variables
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DNN architecture diagram
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Work flow chart
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33])
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Global terrestrial heat flow value (the selected area is in the black box,modified according to Gosnold W[33])
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Measured earth heat flow in a region
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Calculation results of Kriging algorithm and machine learning method for heat flow in a region (left) and their correlation (right)
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Calculation results of terrestrial heat flow in Songliao basin
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36]
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Measured contour map of geothermal gradient in Songliao Basin[36]
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Relative sensitivity of global geological characteristic parameters to heat flow
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