Machine learning-based calculation and characteristic analysis of terrestrial heat flow in the Songliao Basin
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摘要: 大地热流是地球内部热量在地表的直接显示,对地热资源评估具有极高的参考价值,由于传统利用钻井技术的热流测定方法既昂贵又困难,至今松辽盆地仍未能实现高质量、高分辨率的大地热流测量。机器学习是一种用于数据分析的技术,它可以识别数据中的模式并将其用于自动计算未知数据。本文引入机器学习方法来计算区域大地热流。这项研究基于全球大地热流实测数据与地质构造数据,首先采用了Kriging回归算法和机器学习算法计算某已知热流分布区域的大地热流,并计算了均方根误差和相关系数,表明机器学习算法能获得误差更小、相关度更高的结果。随后使用机器学习方法计算了松辽盆地的大地热流值。计算结果显示盆地中部大地热流最高,以大庆、松原之间的区域为中心呈环状向外逐渐变低,中心区域大地热流超过80 mV/m2。该结果与区域实测地温梯度测量结果具有良好一致性,为进一步分析松辽盆地地热资源分布规律提供参考。最后,利用Sobol方法进行地质特征灵敏度分析,量化各参数的影响。本文的研究表明机器学习方法在大地热流值计算方面具有较高的研究和应用价值。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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