基于机器学习与垂向分层建模联合驱动的华北克拉通地温梯度空间分布预测

doi:10.13745/j.esf.sf.2025.4.67

地学前缘 ›› 2025, Vol. 32 ›› Issue (4): 291-302.DOI: 10.13745/j.esf.sf.2025.4.67

• 人工智能驱动地学知识发现 • 上一篇下一篇

基于机器学习与垂向分层建模联合驱动的华北克拉通地温梯度空间分布预测

李金明¹(), 张杨¹, 成秋明¹^,²^,^*()

1.中山大学地球科学与工程学院, 广东珠海 519000
2.中国地质大学(北京) 地质过程与成矿预测全国重点实验室, 北京 100083

收稿日期:2025-04-25 修回日期:2025-05-27 出版日期:2025-07-25 发布日期:2025-08-04
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、矿产普查与勘探领域的研究。E-mail: qiuming.cheng@iugs.org
作者简介:李金明(1998—),男,硕士研究生,主要从事数学地球科学领域的研究。E-mail: lijm227@mail2.sysu.edu.cn
基金资助:
国家自然科学基金项目(42050103);国家自然科学基金项目(42430111);广东省“珠江人才计划”引进创新创业团队项目:大数据-数学地球科学与极端地质事件团队(2021ZT09H399)

Spatial distribution prediction of geothermal gradient in North China Craton driven by the combination of machine learning and stratification modeling

LI Jinming¹(), ZHENG Yang¹, CHENG Qiuming¹^,²^,^*()

1. School of Earth Sciences and Engineering, Sun Yat-Sen University, Zhuhai 519000, China
2. State Key Laboratory of Geological Processes and Mineralization Prediction, China University of Geosciences (Beijing), Beijing 100083, China

Received:2025-04-25 Revised:2025-05-27 Online:2025-07-25 Published:2025-08-04

摘要/Abstract

摘要：

地温梯度作为表征岩石圈热状态的关键参数,其空间分布研究对于理解克拉通热结构演化机制和指导地热资源勘探具有重要意义。传统华北克拉通地温梯度研究多限于一维平面分布特征,未能充分揭示其垂向分异规律,导致预测模型精度受限。本研究创新性地构建了华北克拉通地温梯度深度分层预测模型,系统地阐明其深度依赖模式和热构造控制因素。基于全球热流数据库及前人研究成果,本研究整合了573个具有实测深度信息的地温梯度数据点,将其划分为6个深度段:<500 m,500~1 000 m,1 000~2 000 m,2 000~3 000 m,3 000~4 000 m,>4 000 m。选取居里面深度、大地热流值等13项地质-地球物理特征参数用来训练不同深度段的机器学习回归模型。研究显示:(1)模型预测性能具有显著深度依赖性,中浅部(0~3 000 m)R²>0.45,而深部因样本量减少精度下降;(2)特征重要性分析表明居里面深度与大地热流值对深部预测贡献显著(权重>40%),而磁异常和地质年代等影响较弱(<10%);(3)地温梯度三维分布呈现规律性垂向变化,即浅层高值区(>35 ℃/km)沿活动断裂带分布,中深层(500~3 000 m)高值带自西向东迁移,与太平洋板块俯冲引起的地壳减薄区具有空间耦合性。本研究首次尝试了华北克拉通地温梯度的三维建模,其成果不仅为区域地热资源评估提供了数据支撑,更为理解克拉通破坏过程中的热-构造耦合机制提供了新的观测约束。提出的深度分层建模方法为类似构造单元的热状态研究提供了范式参考。

关键词: 华北克拉通, 地热梯度, 机器学习, 地质与地球物理特征, 分层建模

Abstract:

As a key parameter characterizing the lithospheric thermal state, the spatial distribution of the geothermal gradient is crucial for understanding cratonic thermal evolution and guiding geothermal exploration. Previous studies on the North China Craton (NCC) were largely limited to one-dimensional analyses, failing to resolve vertical variations and limiting model accuracy. This study innovatively develops a depth-stratified prediction model for the NCC geothermal gradient, systematically elucidating its depth-dependent patterns and thermo-tectonic controls. Integrating 573 geothermal gradient measurements with depth information from global databases and literature, data were partitioned into six depth intervals: <500 m, 500-1000 m, 1000-2000 m, 2000-3000 m, 3000-4000 m, and >4000 m. Thirteen geological/geophysical predictors (e.g., Moho depth, geothermal heat flow) were used to train machine learning regression models for each interval. Key results show: (1) Model performance (R²) is depth-dependent: exceeding 0.45 in middle and shallower intervals (<3000 m), but declining significantly at greater depths due to sparse data; (2) Feature importance analysis reveals Moho depth and heat flow dominate deep predictions (weight>40%), while magnetic anomalies and geological age exert minor influence (<10%); (3) The 3D geothermal structure shows systematic depth variations: uppermost high-value zones (>35 ℃/km) align with active fault systems, while mid-deep high-value zones (500-3000 m) migrate eastward, correlating spatially with crustal thinning zones associated with Pacific Plate subduction. This work presents the first 3D geothermal gradient model of the NCC. Results provide critical data for regional geothermal assessment and new constraints on thermo-tectonic coupling during craton destruction. The depth-stratified framework offers a methodological paradigm for thermal studies of analogous tectonic units.

Key words: North China Craton, geothermal gradient, machine learning, geological and geophysical features, stratification modeling

中图分类号:

P314.3
P628

李金明, 张杨, 成秋明. 基于机器学习与垂向分层建模联合驱动的华北克拉通地温梯度空间分布预测[J]. 地学前缘, 2025, 32(4): 291-302.

LI Jinming, ZHENG Yang, CHENG Qiuming. Spatial distribution prediction of geothermal gradient in North China Craton driven by the combination of machine learning and stratification modeling[J]. Earth Science Frontiers, 2025, 32(4): 291-302.

图/表 10

图1 研究区样本点分布(研究区华北克拉通边界(黑线)数据来源于潘桂棠等[6];红星表示的钻孔井位的数据来源于中国大陆科学钻探项目[7]) 背景图为数字地形图,使用GMT6制作[5]。

Fig.1 Distribution of sample points in the study area. The background map is the digital topographic map made with GMT6[5], the boundary of the North China Craton (black line) indicates the study area (data from [6]), and the red star indicates a drill hole (data from [7]).

表1 不同深度层段地热梯度样本数据统计结果

Table 1 Statistical results of thermal gradient sample data in different depth segments

分组	深度/m	样本数量/个	占比
A	0~<500	256	44.68%
B	500~<1 000	231	40.31%
C	1 000~<2 000	208	36.30%
D	2 000~<3 000	150	26.18%
E	3 000~4 000	81	14.14%
F	>4 000	22	3.84%

图2 各深度层段地热梯度值的直方图红线为正态分布曲线。

Fig.2 Histogram of thermal gradient values for each depth segments. The red line represents the normal distribution curve.

表2 地质与地球物理特征及其数据来源

Table 2 Geological and geophysical features used in the study and data sources

编号	特征名称	数据来源
1	地质年代	[28]
2	磁异常	[29]
3	地壳厚度	[30]
4	结晶壳厚度	[30]
5	沉积物厚度	[30]
6	岩石圈厚度	[31]
7	均衡重力异常	[32]
8	布格重力异常	[32]
9	地形	[33]
10	自由空气重力异常	[34]
11	居里点深度	[35]
12	距火山距离	[36]
13	大地热流	[12]

图3 不同深度段所对应最佳算法模型的评价指标统计图

Fig.3 Statistical chart of the performance evaluation standard value of the optimal algorithm model corresponding to different depth Segments A: 0~<500 m; B: 500~<1 000 m; C: 1 000~<2 000 m; D: 2 000~<3 000 m; E: 3 000~4 000 m; F: >4 000 m。

图4 实际测量值与预测值散点图蓝点为各深度段所有的观测值,红线为最小二乘法拟合的直线,浅红色条带为95%的置信区间。

Fig.4 Scatter plot of actual measured value and predicted values. The blue dots represent all the observed values of each depth segments, the red line is the straight line fitted by the least square method, and the light red band is the 95% confidence interval.

图5 各深度层段特征重要性排序统计折线图 a—不同深度的大地热流与居里点深度特征重要性变化;b—不同深度的沉积物厚度、结晶地壳厚度和地壳厚度特征重要性变化;c—不同深度地壳厚度与均衡重力异常特征重要性变化;d—不同深度的布格重力异常和自由空气重力异常特征重要性变化;e—不同深度岩石圈厚度与地形特征重要性变化;f—不同深度均衡重力异常与到火山的距离特征重要性变化。A: 0~<500 m;B: 500~<1 000 m;C: 1 000~<2 000 m;D: 2 000~<3 000 m;E: 3 000~4 000 m;F: >4 000 m。

Fig.5 Statistical line chart of feature importance ranking of each depth segment. a—the variations in the importance of geothermal heat flow and Curie point depth features at different depths; b—the variations in the importance of sediment thickness, crystalline crust thickness and crustal thickness features at different depths; c—the variations in the importance of crustal thickness and isostatic gravity anomaly features at different depths; d—the variations in the importance of bouguer gravity anomaly and free air gravity anomaly features at different depths; e—the variations in the importance of the thickness of lithosphere and topography features at different depths; f—the variations in the importance of isostatic gravity anomaly and the distance to volcano features at different depths.

图6 6个深度段的预测地热梯度分布图(研究区华北克拉通边界(黑线)数据来源于潘桂棠等[6]) 图中背景为制图软件ArcGis pro中的山体阴影。

Fig.6 Predicted geothermal gradient distribution maps of six depth segments. The boundary of the North China Craton (black line) indicates the study area (data from [6]), and the background is the mountain shadow in the mapping software ArcGis pro.

图7 活动断裂与0~500 m地热梯度分布(研究区华北克拉通边界(黑线)数据来源于潘桂棠等[6];红线为活动断裂分布数据来源于Styron和Pagani[48]) 图中背景为制图软件ArcGis pro中的山体阴影。

Fig.7 Active faults and 0-500 m geothermal gradient distribution. The boundary of the North China Craton (black line) indicates the study area (data from [6]), the background is the mountain shadow in the mapping software ArcGis pro, and the red line represents the distribution of active faults (data from [48]).

图8 CCSD-1井温与预测温度曲线(数据来源于中国大陆科学钻探项目[7]) 图中黑线为钻井温度曲线,蓝线为井温达到稳定后的井温曲线,红线为本研究预测地热梯度计算得出的该点位地温-深度变化曲线。井位如图1所示。

Fig.8 Comparison of measured temperature and predicted temperature curves for the CCSD-1 well (data from CCSDP). The well location is shown in Fig.1. The black line represents the drilling temperature curve, the blue line represents the well temperature curve after the well temperature reaches stability, and the red line represents the temperature-depth variation curve at this point calculated by predicting the geothermal gradient in this study.

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基于机器学习与垂向分层建模联合驱动的华北克拉通地温梯度空间分布预测

Spatial distribution prediction of geothermal gradient in North China Craton driven by the combination of machine learning and stratification modeling

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