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

doi:10.13745/j.esf.sf.2025.4.56

Abstract

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

CLC Number:

P612
P628

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.

Figures/Tables 13

References 52

[1]	KEMP E D. 3-D geological modelling supporting mineral exploration. Mineral deposits of Canada: a synthesis of major deposit types, district metallogeny, the evolution of geological provinces, and exploration methods[C]. Ottawa: Geological Association of Canada, Mineral Deposits Division, 2007: 1051-1061.
[2]	陈建平, 吕鹏, 吴文, 等. 基于三维可视化技术的隐伏矿体预测[J]. 地学前缘, 2007, 14(5): 54-62.
[3]	毛先成, 邹艳红, 陈进, 等. 危机矿山深部、边部隐伏矿体的三维可视化预测: 以安徽铜陵凤凰山矿田为例[J]. 地质通报, 2010, 29(增刊1): 401-413.
[4]	XIAO K Y, LI N, PORWAL A, et al. Research on GIS-based 3D prospectivity mapping and a case study of Jiama copper-polymetallic deposit in Tibet, China[J]. Ore Geology Reviews, 2015, 71, 611-632.
[5]	WANG G W, LI R X, CARRANZA E J M, et al. 3D geological modeling for prediction of subsurface Mo targets in the Luanchuan district, China[J]. Ore Geology Reviews, 2015, 71: 592-610.
[6]	NIELSEN S H H, CUNNINGHAM F, HAY R, et al. 3D prospectivity modelling of orogenic gold in the Marymia Inlier, Western Australia[J]. Ore Geology Reviews, 2015, 71: 578-591.
[7]	韩润生, 刘飞, 张艳. 论热液成矿系统中构造体系控矿作用[J]. 地学前缘, 2025, 32(2): 371-389. DOI
[8]	周琦, 吴冲龙. 基于大数据的智慧探矿模式实验研究与进展[J]. 地学前缘, 2024, 31(6): 350-367. DOI
[9]	赵鹏大. 大数据时代数字找矿与定量评价[J]. 地质通报, 2015, 34(7): 1255-1259.
[10]	陈建平, 李婧, 崔宁, 等. 大数据背景下地质云的构建与应用[J]. 地质通报, 2015, 34(7): 1260-1265.
[11]	王登红, 刘新星, 刘丽君. 地质大数据的特点及其在成矿规律、成矿系列研究中的应用[J]. 矿床地质, 2015, 34(6): 1143-1154.
[12]	肖克炎, 孙莉, 李楠, 等. 大数据思维下的矿产资源评价[J]. 地质通报, 2015, 34(7): 1266-1272.
[13]	周永章, 黎培兴, 王树功, 等. 矿床大数据及智能矿床模型研究背景与进展[J]. 矿物岩石地球化学通报, 2017, 36(2): 327-331, 344.
[14]	周永章, 陈烁, 张旗, 等. 大数据与数学地球科学研究进展: 大数据与数学地球科学专题代序[J]. 岩石学报, 2018, 34(2): 255-263.
[15]	赵鹏大, 胡旺亮, 李紫金. 矿床统计预测[M]. 2版. 北京: 地质出版社, 1994.
[16]	余先川, 刘立文, 胡丹, 等. 基于稳健有序独立成分分析(ROICA)的矿产预测[J]. 吉林大学学报(地球科学版), 2012, 42(3): 872-880.
[17]	毛先成, 邓浩, 陈进, 等. 金属矿山深部资源三维智能预测理论与方法[J]. 矿产勘查, 2024, 15(8): 1365-1378.
[18]	AGTERBERG F P, BONHAM-CARTER G F. Deriving weights-of-evidence from geoscience contour maps for prediction of discrete events[C]// Proceedings of the 2nd APCOM Symposium. Berlin:APCOM, 1990: 381-395.
[19]	CARRANZA E J M. Weights of evidence modeling of mineral potential: a case study using small number of prospects, abra, Philippines[J]. Natural Resources Research, 2004, 13(3): 173-187.
[20]	毛先成, 邹品娟, 曹芳, 等. GIS支持下的线性回归证据权法扩展及成矿预测[J]. 测绘科学, 2013, 38(3): 18-21.
[21]	CARRANZA E J M. Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines[J]. Exploration and Mining Geology, 2001, 10(3): 165-175.
[22]	PORWAL A, CARRANZA E J M. Classifiers for modeling of mineral potential[M]. Chichester: John Wiley & Sons, 2008, 149-171.
[23]	黄继先, 毛先成, 陈进, 等. 基于GWR的丁家山铅锌矿控矿因素影响研究[J]. 地质学刊, 2017, 41(3): 401-408.
[24]	DENG H, ZHENG Y, CHEN J, et al. Learning 3D mineral prospectivity from 3D geological models using convolutional neural networks: application to a structure-controlled hydrothermal gold deposit[J]. Computers & Geosciences, 2022, 161: 105074.
[25]	WANG Z Y, ZUO R G. Mineral prospectivity mapping using a joint singularity-based weighting method and long short-term memory network[J]. Computers & Geosciences, 2022, 158: 104974.
[26]	XIONG Y H, ZUO R G. Robust feature extraction for geochemical anomaly recognition using a stacked convolutional denoising autoencoder[J]. Mathematical Geosciences, 2022, 54(3): 623-644.
[27]	FOTHERINGHAM A S, CHARLTON M E, BRUNSDON C. Spatial variations in school performance: a local analysis using geographically weighted regression[J]. Geographical and Environmental Modelling, 2001, 5(1): 43-66.
[28]	FOTHERINGHAM A S, BRUNSDON C, CHARLTON M. Geographically weighted regression: the analysis of spatially varying relationships[M]. 1st ed.ed. Chichester: Wiley, 2002.
[29]	ZHANG D J, REN N, HOU X H. An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping[J]. Geoscientific Model Development, 2018, 11(6): 2525-2539.
[30]	LIU Y C, LI Z X, LAUKAMP C, et al. Quantified spatial relationships between gold mineralisation and key ore genesis controlling factors, and predictive mineralisation mapping, St Ives Goldfield, Western Australia[J]. Ore Geology Reviews, 2013, 54: 157-166.
[31]	ZHAO J, WANG W L, CHENG Q M. Investigation of spatially non-stationary influences of tectono-magmatic processes on Fe mineralization in eastern Tianshan, China with geographically weighted regression[J]. Journal of Geochemical Exploration, 2013, 134: 38-50.
[32]	ZHAO J, WANG W L, CHENG Q M. Application of geographically weighted regression to identify spatially non-stationary relationships between Fe mineralization and its controlling factors in eastern Tianshan, China[J]. Ore Geology Reviews, 2014, 57: 628-638.
[33]	HUANG J X, MAO X C, CHEN J, et al. Exploring spatially non-stationary relationships in the determinants of mineralization in 3D geological space[J]. Natural Resources Research, 2020, 29(1): 439-458.
[34]	袁峰, 李晓晖, 田卫东, 等. 三维成矿预测关键问题[J]. 地学前缘, 2024, 31(4): 119-128. DOI
[35]	LIU Z K, YU S Y, DENG H, et al. 3D mineral prospectivity modeling in the Sanshandao gold field, China using the convolutional neural network with attention mechanism[J]. Ore Geology Reviews, 2024, 164: 105861.
[36]	SONG M C, DING Z J, ZHANG J J, et al. Geology and mineralization of the Sanshandao super giant gold deposit (1200 t) in the Jiaodong Peninsula, China: a review[J]. China Geology, 2021, 4(4): 686-719.
[37]	王岩, 王登红, 王成辉, 等. 基于地质大数据的中国金矿时空分布规律定量研究[J]. 地学前缘, 2024, 31(4): 438-455. DOI
[38]	宋英昕, 李胜荣, 申俊峰, 等. 胶东三山岛北部海域金矿床石英热释光和晶胞参数特征及其找矿意义[J]. 地学前缘, 2021, 28(2): 305-319. DOI
[39]	刘殿浩, 吕古贤, 张丕建, 等. 胶东三山岛断裂构造蚀变岩三维控矿规律研究与海域超大型金矿的发现[J]. 地学前缘, 2015, 22(4): 162-172. DOI
[40]	毛先成, 王迷军, 刘占坤, 等. 基于勘查数据的胶东大尹格庄金矿床控矿地质因素定量分析[J]. 地学前缘, 2019, 26(4): 84-93. DOI
[41]	杨立强, 邓军, 王中亮, 等. 胶东中生代金成矿系统[J]. 岩石学报, 2014, 30(9): 2447-2467.
[42]	宋英昕, 宋明春, 丁正江, 等. 胶东金矿集区深部找矿重要进展及成矿特征[J]. 黄金科学技术, 2017, 25(3): 4-18. DOI
[43]	宋明春, 张军进, 张丕建, 等. 胶东三山岛北部海域超大型金矿床的发现及其构造-岩浆背景[J]. 地质学报, 2015, 89(2): 365-383.
[44]	HUANG J X, MAO X C, DENG H, et al. An improved GWR approach for exploring the anisotropic influence of ore-controlling factors on mineralization in 3D space[J]. Natural Resources Research, 2022, 31(4): 2181-2196.
[45]	FOTHERINGHAM A S, YANG W B, KANG W. Multiscale geographically weighted regression (MGWR)[J]. Annals of the American Association of Geographers, 2017, 107(6): 1247-1265.
[46]	HUANG J X, LIU Z K, DENG H, et al. Exploring multiscale non-stationary influence of ore-controlling factors on mineralization in 3D geological space[J]. Natural Resources Research, 2022, 31(6): 3079-3100.
[47]	ZHANG L, GROVES D I, YANG L Q, et al. Relative roles of formation and preservation on gold endowment along the Sanshandao gold belt in the Jiaodong gold province, China: importance for province- to district-scale gold exploration[J]. Mineralium Deposita, 2020, 55(2): 325-344.
[48]	翟裕生. 区域构造、地球化学与成矿[J]. 地质调查与研究, 2003, 26(1): 1-7.
[49]	翟裕生. 关于构造—流体—成矿作用研究的几个问题[J]. 地学前缘, 1996, 3(4): 230-236.
[50]	李瑞翔, 高书剑, 薛冰, 等. 胶东三山岛超巨型金矿床三维地质模型及深部矿体与断裂的耦合关系[J]. 地质通报, 2022, 41(6): 968-976.
[51]	宋明春, 丁正江, 张军进, 等. 胶东三山岛超巨型金矿床及断裂与矿体耦合关系[C]// 首届全国矿产勘查大会论文集. 合肥: 中国地球物理学会, 2021: 967-970.
[52]	BRUNSDON C, FOTHERINGHAM A S, CHARLTON M. Geographically weighted summary statistics: a framework for localised exploratory data analysis[J]. Computers, Environment and Urban Systems, 2002, 26(6): 501-524.

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

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

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

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

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^*