基于勘查大数据的控矿作用空间非平稳性定量研究:以三山岛金矿床为例

doi:10.13745/j.esf.sf.2025.4.56

地学前缘 ›› 2025, Vol. 32 ›› Issue (4): 317-328.DOI: 10.13745/j.esf.sf.2025.4.56

• 人工智能驱动地学知识发现 • 上一篇下一篇

基于勘查大数据的控矿作用空间非平稳性定量研究:以三山岛金矿床为例

黄继先¹^,²(), 李苇琪¹^,², 邓浩¹^,²^,^*(), 万世军¹^,², 李晓³, 毛先成¹^,²

1.有色金属成矿预测与地质环境监测教育部重点实验室(教育部), 中南大学地球科学与信息物理学院, 湖南长沙 410083
2.湖南有色资源与地质灾害探查重点实验室, 湖南长沙 410083
3.招金矿业股份有限公司, 山东烟台 265400

收稿日期:2025-01-15 修回日期:2025-04-21 出版日期:2025-07-25 发布日期:2025-08-04
通信作者: *邓浩(1983—),男,博士,副教授,博士生导师,主要从事三维成矿预测相关工作。E-mail: haodeng@csu.edu.cn
作者简介:黄继先(1973—),女,博士,讲师,主要从事三维空间分析与三维成矿预测相关工作。E-mail: jxhuang@csu.edu.cn
基金资助:
国家自然科学基金面上项目(42030809);国家自然科学基金面上项目(42172328);国家自然科学基金面上项目(42272344);国家自然科学基金面上项目(42072325);国家自然科学基金面上项目(42202332)

Quantitative study on spatial non-stationarity of ore-controlling processes based on exploration big data: A case study of Sanshandao gold deposit

HUANG Jixian¹^,²(), LI Weiqi¹^,², DENG Hao¹^,²^,^*(), WAN Shijun¹^,², LI Xiao³, MAO Xiancheng¹^,²

1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha 410083, China
3. Zhaojin Mining Industry Co., Ltd., Yantai 265400, China

Received:2025-01-15 Revised:2025-04-21 Online:2025-07-25 Published:2025-08-04

摘要/Abstract

摘要：

隐伏矿体三维预测正在逐渐成为地球深部矿产资源勘查的关键技术和方法,准确把握控矿因素与矿化的关联关系对模型预测性能至关重要。矿化的形成是一个典型的空间非平稳过程,利用大数据技术,从勘查数据出发,定量探索控矿因素与矿化的空间非平稳关系及其特征,可为三维成矿预测建模提供更精准的关键参数,有助于从定量的角度厘清控矿作用规律及其背后的成因。本文以三山岛金矿床为研究实例,对控矿作用的空间非平稳性及其特征展开研究,首先利用三维地理加权回归(geographical weighted regression,GWR)模型探测控矿因素对矿化影响的空间非平稳性;随后通过方向加权改进三维GWR的权函数,以此为基础分析控矿作用的各向异性;然后将多尺度GWR模型扩展到三维空间,针对不同控矿因素研究其对矿化影响的多尺度特征;接下来通过计算不同控矿因素对矿化影响的平稳性指数,比较分析其平稳性程度;最后对不同控矿因素对矿化的影响强度与变异程度进行对比分析,结合成矿规律进一步挖掘了各控矿因素对矿化影响的方向、尺度及强度特征的相互关联关系。

关键词: 空间非平稳性, 地理加权回归, 各向异性, 多尺度GWR, 三维成矿预测

Abstract:

Three-dimensional prediction of concealed ore bodies is gradually becoming a key technology and method for mineral resources exploration in the deep earth. Accurately calibrating the association between ore-controlling factors and mineralization is crucial to the performance of prediction model. The formation of mineralization is a typical spatial non-stationary process. Based on exploration data, leveraging big data technology to quantitatively investigate the spatial non-stationarity in the relationship between ore-controlling factors and mineralization can not only provide more accurate key parameters for prediction model, but help clarify the genetic mechanism behind mineralization. In this paper, we take the Sanshandao gold deposit as an example to study the characteristics of spatial non-stationary influence of ore-controlling factors on mineralization. First, the 3D Geographical Weighted Regression (GWR) model is introduced to explore the spatial non-stationary influence. Second, the anisotropy is analyzed by introducing the directional factor to the weight calculation of 3D GWR. Third, the multi-scale characteristic is explored by applying the 3D multiscale GWR model (MGWR). Furthermore, the stationary index is calculated to analyze the stationarity degree of different ore-controlling factors’ influence. Subsequently, a comparative analysis of the intensity and variability of different ore-controlling factors’ influence is carried out. Finally, the interrelationship among direction, scale and intensity of each ore-controlling factor’s influence on the mineralization is further explored based on the mineralization laws.

Key words: spatial non-stationarity, geographical weighted regression, anisotropic effect, multi-scale geographical weighted regression, 3D metallogenic prognosis

中图分类号:

P612
P628

黄继先, 李苇琪, 邓浩, 万世军, 李晓, 毛先成. 基于勘查大数据的控矿作用空间非平稳性定量研究:以三山岛金矿床为例[J]. 地学前缘, 2025, 32(4): 317-328.

HUANG Jixian, LI Weiqi, DENG Hao, WAN Shijun, LI Xiao, MAO Xiancheng. Quantitative study on spatial non-stationarity of ore-controlling processes based on exploration big data: A case study of Sanshandao gold deposit[J]. Earth Science Frontiers, 2025, 32(4): 317-328.

图/表 13

图1 三山岛金矿地质图和勘探线联合剖面图(据文献[35-36]修改) a—三山岛金矿地质图;b—勘探线联合剖面图。

Fig.1 Geological map of Sanshandao gold deposit and combined geological sections. Modified after [35-36].

图2 三山岛金矿床金属量空间分布图

Fig.2 The spatial distribution of metal content of the Sanshangdao gold deposit

表1 数据统计

Table 1 Data statistics

变量	平均值	中位数	标准偏差	最小值	最大值
dF	-40.08	-30.26	50.78	-268.93	120.93
fV	0.46	-0.07	7.19	-41.13	71.82
gF	45.98	44.29	11.65	7.17	90.00
waF	-2.55	-0.15	15.54	-184.47	106.49
wbF	-3.06	-0.23	30.34	-140.31	119.29
AuMet (t)	12 587.13	2 904.65	33 665.47	0.00	1 004 561.06

表2 多元共线性诊断

Table 2 Multi-collinearity diagnosis

变量	dF	waF	wbF	gF	fV
容差	0.958	0.515	0.938	0.742	0.548
VIF	1.043	1.940	1.066	1.348	1.824

表3 OLS诊断结果

Table 3 Result of OLS diagnosis

观测点数	34 569	Akaike’s Information Criterion (AICc) [d]:	817 772.111
R²	0.030	调整R²	0.029 9
联合 F统计量 [e]	213.953	Prob(>F), (5, 103 752) 自由度	0.000 000^*
联合卡方统计量 [e]	756.787	Prob(>chi-squared), (5)自由度	0.000 000^*
Koenker (BP)统计量[f]	104.087	Prob(>chi-squared), (5)自由度	0.000 000^*
Jarque-Bera 统计量[g]	85 635 436.864	Prob(>chi-squared), (2)自由度	0.000 000^*

图3 方向距离αij示意图(据文献[44])

Fig.3 Sketch map of directional distance. Adapted from [44].

表4 各模型性能与全局空间自相关

Table 4 Model performance and global spatial autocorrelation

模型	R²	全局 Moran指数	Z值
OLS	0.03	0.075	3.53
GWR	0.78	0.033	1.55
GDWR	0.85	0.032	1.54
MGWR	0.86	0.031	1.48

图4 OLS、GWR、GDWR和MGWR模型残差局部空间自相关指数空间分布

Fig.4 Local Moran’s I for residuals of model OLS,GWR,GDWR and MGWR a—OLS;b—GWR;c—GDWR;d—MGWR。

图5 控矿作用各向异性分布图和流体汇聚方向示意图(对应剖面图1b,据文献[42]) a—控矿作用各向异性分布图;b—流体汇聚方向示意图。

Fig.5 Anisotropic pattern of ore-controlling process and fluid convergence directions. Corresponding to Fig.1b and adapted from [42].

表5 GWR与MGWR模型带宽

Table 5 Bandwidth for models GWR and MGWR

模型	最小值	最大值	beta0	beta_dF	beta_waF	beta_wbF	beta_gF	beta_fV
GWR	30	34 000	70
MGWR	30	34 000	30	30	4 130	7 030	26 830	33 930

表6 空间平稳性指数

Table 6 Spatial stationary index for different ore-controlling factors

模型	dF	waF	wbF	gF	fV
GWR	17.2	33.1	17.2	25.8	44.1
GDWR	21.4	29.8	17.4	24.1	40.2
MGWR	8.9	4.2	1.1	0.7	0.1

图6 控矿因素影响系数箱形图和空间分布

Fig.6 Box plot and spatial distributions of parameters (a-1),(a-2):beta_dF;(b-1),(b-2):beta_waF;(c-1),(c-2):beta_wbF;(d-1),(d-2):beta_fV;(e-1),(e-2):beta_gF。

表7 平均影响强度与变异系数

Table 7 Average influence intensity and coefficient of variation

影响系数	平均影响强度	变异系数/%
beta_dF	1 753 102.3	250.7
beta_waF	489 600.9	44.8
beta_wbF	1 138 760.5	16.2
beta_gF	77 216.6	55.6
beta_fV	1 368 735.4	0.4

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基于勘查大数据的控矿作用空间非平稳性定量研究:以三山岛金矿床为例

Quantitative study on spatial non-stationarity of ore-controlling processes based on exploration big data: A case study of Sanshandao gold deposit

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