摘要
大地热流是地球动力学研究与资源勘查的重要指标,但其测量易受气候、热液活动等因素的干扰,导致实测数据匮乏。本研究针对现有基于机器学习的热流预测模型对洋壳热流特异性考虑不足的问题,整合南海实测热流和多源地质、地球物理等数据,基于线性模型、支持向量机和XGBoost算法,通过对比引入与不引入洋壳特征(距洋中脊距离和洋壳年龄)的预测模型,揭示洋壳特征对南海热流分布的影响机制。结果显示:洋壳特征虽与实测热流值无显著相关性,但其使预测热流在洋中脊附近呈现更显著的带状分布;且在引入洋壳特征的模型中,布格重力异常的特征重要性显著高于其他特征。基于热流值、布格重力异常和距洋中脊距离的K-means聚类分析识别出构造主导型洋壳(类型1)与岩浆主导型洋壳(类型2)区域,印证南海洋壳扩张自23.6 Ma洋中脊跃迁后扩张机制由岩浆主导向构造主导的演化特征。本研究为南海深部动力学过程提供了数据驱动的新认知框架。
Heat flow is a critical parameter in geodynamic studies and resource exploration,but its measurements are often susceptible to interference from factors such as climate and hydrothermal activity,resulting in scarce observational data.This study addresses the insufficient consideration of oceanic crust specificity in existing machine learning-based heat flow prediction models.By integrating measured heat flow and multi-source geological and geophysical data from the South China Sea,we employ Linear Model,Support Vector Machine,and XGBoost algorithms to compare prediction models with and without oceanic crust features(distance to mid-ocean ridge and oceanic crust age),revealing the influence mechanisms of oceanic crust characteristics on the regional heat flow distribution.The results show that oceanic crust features exhibit no significant correlation with measured heat flow values,yet they result in a more pronounced zonal distribution of predicted heat flow near the mid-ocean ridge.In models incorporating these features,the feature importance of Bouguer gravity anomaly becomes significantly higher than that of other features.Furthermore,K-means clustering analysis based on heat flow values,Bouguer gravity anomaly,and distance to mid-ocean ridge identifies two distinct oceanic crust types:tectonically dominated(Type 1)and magmatically dominated(Type 2)regions.This supports the evolutionary trend of the South China Sea’s oceanic crust spreading mechanism shifting from magmatically to tectonically dominated after the mid-ocean ridge jump at 23.6 Ma.This study provides a new data-driven framework for understanding the deep dynamic processes of the South China Sea.
作者
张雨飞
张杨
吉俊杰
成秋明
ZHANG Yufei;ZHANG Yang;JI Junjie;CHENG Qiuming(School of Earth Science and Engineering,Sun Yat sen University,Zhuhai 519082,China;State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences,Beijing 100083,China)
出处
《地学前缘》
北大核心
2025年第4期235-249,共15页
Earth Science Frontiers
基金
国家自然科学基金项目(42050103)
广东省珠江创新项目(2021ZT09H399)
国家自然科学基金重点项目(4243011)。
关键词
大地热流
南海
机器学习
洋壳
洋中脊
geothermal heat flow
South China Sea
machine learning
oceanic crust
mid-ocean ridge
作者简介
张雨飞(2000-),男,硕士研究生,主要从事数学地球科学领域的研究。E-mail:zhangyf325@mail2.sysu.edu.cn;通信作者:成秋明(1960-),男,教授,博士生导师,中国科学院院士,主要从事数学地球科学领域的研究。E-mail:qiuming.cheng@iugs.org。
