基于机器学习方法的南海洋壳大地热流预测

doi:10.13745/j.esf.sf.2025.4.60

摘要/Abstract

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。

关键词: 大地热流, 南海, 机器学习, 洋壳, 洋中脊

Abstract:

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.

Key words: geothermal heat flow, South China Sea, machine learning, oceanic crust, mid-ocean ridge

中图分类号:

P314.3

张雨飞, 张杨, 吉俊杰, 成秋明. 基于机器学习方法的南海洋壳大地热流预测[J]. 地学前缘, 2025, 32(4): 235-249.

ZHANG Yufei, ZHANG Yang, JI Junjie, CHENG Qiuming. Prediction of lithospheric heat flow of the South China Sea Oceanic crust based on machine learning methods[J]. Earth Science Frontiers, 2025, 32(4): 235-249.

图/表 16

图1 南海地形和各构造块体分区(地形数据据文献[24],构造单元界线数据、南海微板块边界和南海各构造块体据文献[25],1、2和3号洋中脊数据据文献[26])

Fig.1 Topography of the South China Sea and division of tectonic blocks. Terrain data according to reference [24]; Boundary data of tectonic units, microplate boundaries of the South China Sea, and tectonic blocks of the South China Sea are based on reference [25]; data for mid-ocean ridges 1, 2, and 3 are from reference [26].

图2 研究区域与实测热流点

Fig.2 The study area and measured heat flow points

图3 大地热流测量值频率分布直方图

Fig.3 Histogram of the frequency distribution of heat flow measurements

表1 南海各洋盆实测热流统计

Table 1 Statistics of measured heat flow in various ocean basins in the South China Sea

各次级洋盆	数量/个	最大值/(mW·m^-2)	最小值/(mW·m^-2)	平均值/(mW·m^-2)	标准值/(mW·m^-2)	中位值/(mW·m^-2)
西北次海盆	10	115.2	8.2	72.8	37.6	86.1
东部次海盆	30	152.0	17.2	88.4	32.6	91.5
西南次海盆	31	152.0	11.1	99.5	27.8	99.5

表2 用于预测大地热流的特征及其来源数据集

Table 2 Characterization and its source datasets for the prediction of heat flow

编号	数据名称	数据来源
1	沉积物厚度	Straume 等^[47]
2	海底测深	Tozer 等^[24]
3	布格重力异常	Balmino等^[48]
4	均衡重力异常	Balmino等^[48]
5	自由空气重力异常	Balmino等^[48]
6	磁异常数据	Choi 等^[49]
7	岩石圈厚度	Hoggard等^[50]
8	结晶地壳(无沉积物)厚度	Mooney等^[51]
9	垂向重力梯度	Sandwell等^[52]
10	居里点深度	Li等^[39]
11	地形崎岖度
12	距海山距离	Yesson 等^[53]
13	距山丘距离	Yesson 等^[53]
14	洋壳年龄	Seton等^[54]
15	距洋中脊距离

图4 大地热流测量值与各特征之间相关性图

Fig.4 Correlation between heat flow measurements and individual characteristics

图5 各次海盆大地热流测量值与各特征之间相关性图

Fig.5 Correlation between the measured values of heat flow and the characteristics of each basin

图6 整体预测结果评价

Fig.6 Evaluation of overall forecasting results

图7 XGBoost方法实际值与预测值的散点图 (a)—不引入洋壳特征;(b)—引入洋壳特征。

Fig.7 Scatterplot of actual versus and predicted values for the XGBoost

图8 南海洋壳热流预测图 (a)—不引入洋壳特征;(b)—引入洋壳特征。

Fig.8 Predicted heat flow map of the South China Sea oceanic crust

图9 随机超参数训练的验证模型 (a)—超参数组合1不引入洋壳特征热流模型;(b)—超参数组合1引入洋壳特征热流模型;(c)—超参数组合2不引入洋壳特征热流模型;(d)—超参数组合2引入洋壳特征热流模型。

Fig.9 Validation model for randomized hyperparameter training

图10 特征重要性排序 (a)—不引入洋壳特征;(b)—引入洋壳特征。

Fig.10 Ranking of feature importance

图11 K-means聚类三维空间结果

Fig.11 3D spatial results of K-means clustering

图12 类型1和类型2区域聚类各要素之间关系探究 (a)—南海洋壳区域布格重力异常与预测热流值关系;(b)—类型1南海洋壳区域布格重力异常与预测热流值密度图;(c)—类型2南海洋壳区域布格重力异常与预测热流值密度图;(d)—南海洋壳区域预测热流值与距洋中脊距离关系;(e)—类型1南海洋壳区域预测热流值与距洋中脊距离密度图;(f)—类型2南海洋壳区域预测热流值与距洋中脊距离密度图;(g)—南海洋壳区域布格重力异常与距洋中脊距离关系;(h)—类型1南海洋壳区域布格重力异常与距洋中脊距离密度图;(i)—类型2南海洋壳区域布格重力异常与距洋中脊距离密度图。

Fig.12 Exploring the relationships between elements in Type 1 and Type 2 regional clusters

图13 类型1和2区域的空间位置示意

Fig.13 Spatial distribution of Type 1 and Type 2 regions

图14 不同类别热流值与各特征之间相关性图

Fig.14 Correlation diagram between different categories of heat flux values and various features

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