Three-dimensional geological modeling and mineral prospectivity mapping in the Weishancheng gold-silver district, Henan, China

doi:10.13745/j.esf.sf.2023.6.8

Abstract

Abstract:

Three-dimensional (3D) geological modeling and mineral prospectivity mapping have currently emerged as widely adopted and well-developed technical approaches. Leveraging insights from the geological features of the Yindongpo gold deposit and the Poshan silver deposit, the application of 3D modeling and mineral prospectivity mapping has been extended to the Weishancheng district. A comprehensive understanding of geological entities (such as strata, rock formations, and ore bodies) and structural configurations has been achieved through the establishment of 82 geological profiles. Geophysical models encompassing density, magnetic susceptibility, and resistivity have been constructed using 1:50000 gravity and magnetic data along with 63 induced polarization datasets. Additionally, a geochemical model has been developed based on 52 rock survey profiles, 614 boreholes, 117 exploratory trenches, 56 shallow wells, and 94-foot drills. Integration of geological, geophysical, and geochemical datasets has been accomplished through the amalgamation of the data and methodologies. Following the principles of the “Trinity” metallogenic prediction theory, a 3D quantitative prediction model for magmatic hydrothermal gold and silver deposits within the district has been formulated, leading to the establishment of exploration criteria for gold and silver deposits. Utilizing weights of evidence, the weight values and correlation degrees of the exploration criteria have been extracted to facilitate mineral prospectivity mapping. The determination of threshold values via C-V fractal analysis has resulted in the identification of six prospective targets. The application of weights of evidence for 3D mineral prospectivity mapping has been validated as a viable and scientifically sound approach. The delineated targets hold significant practical implications for the exploration of concealed ore bodies within the deep and peripheral regions of mining sites.

Key words: Weishancheng district, muti-source data, 3D geological modeling, weights of evidence, mineral prospectivity mapping

CLC Number:

GU Hao, YANG Zeqiang, GAO Meng, TANG Xiangwei, WANG Dongxiao, LIU Kuisong, YANG Shuren, GUO Yueshan, WANG Yun, WANG Gongwen. Three-dimensional geological modeling and mineral prospectivity mapping in the Weishancheng gold-silver district, Henan, China[J]. Earth Science Frontiers, 2024, 31(3): 245-259.

Figures/Tables 18

Fig.1 Geological rough sketch of ore concentration area in Weishancheng. A—the geotectonic location of the ore-concentrated area; B—geological sketch map of ore-concentrated area.

Fig.2 Metallogenic model of gold and silver deposits in Weishancheng Mining Area

Fig.3 3D geological modeling and quantitative prediction technology process

Table 1 Statistical results of errors in magnetic measurement

数据来源	磁测总精度/nT	野外观测均方误差/nT					基点、高程及正常场改正误差/nT
数据来源	磁测总精度/nT	总计	操作及点位差	仪器一致性差	仪器噪声误差	日变改正误差	总计	正常场改正差	高程改正误差	总基点改正差
本次测量	2.02	1.551	1.241	0.872	0.24	0.216	1.293	0.3	0.788	0.98
DZ/T 0071—1993	5	4.36	2.65	2.0	2.0	2.0	2.45	1.0	1.0	2.0

Fig.4 Relationship between 3D geological model and orebody

Fig.5 Three-dimensional geophysics model

Fig.6 Vertical and axial zoning characteristics of ore-forming elements in Yindongpo gold deposit

Fig.7 DSI interpolation model for different geochemical elements

Table 2 Comprehensive information quantitative prediction model

矿床类型	控矿因素		定量化预测要素	矿床实例
岩浆热液型	地层	歪头山组为赋矿岩层,特别是碳质绢云石英片岩为主要赋矿围岩	二段第一岩性层 $w 21$	银洞坡金矿、破山银矿
			二段第二岩性层 $w 22$
			三段第二岩性层 $w 32$
			碳质层
	构造	朱庄背形构造层间破碎带	断裂带
	岩浆岩	隐伏酸性岩体	岩体
	地球物理	矿床出现在负重力异常带上	低密度
		矿床分布在低缓磁异常内	低磁化率
		矿床显示低电阻率、高激化率	低电阻率、高激化率
	地球化学	主要元素组合为Au、Ag、Pb、Zn、As、Sb	Au
			Ag
			Pb
			Zn
			As
			Sb

Table 2 Comprehensive information quantitative prediction model

矿床类型	控矿因素		定量化预测要素	矿床实例
岩浆热液型	地层	歪头山组为赋矿岩层,特别是碳质绢云石英片岩为主要赋矿围岩	二段第一岩性层 $w 21$	银洞坡金矿、破山银矿
			二段第二岩性层 $w 22$
			三段第二岩性层 $w 32$
			碳质层
	构造	朱庄背形构造层间破碎带	断裂带
	岩浆岩	隐伏酸性岩体	岩体
	地球物理	矿床出现在负重力异常带上	低密度
		矿床分布在低缓磁异常内	低磁化率
		矿床显示低电阻率、高激化率	低电阻率、高激化率
	地球化学	主要元素组合为Au、Ag、Pb、Zn、As、Sb	Au
			Ag
			Pb
			Zn
			As
			Sb

Table 3 Statistical table of ore-bearing property of different ore-bearing strata

矿种	赋矿地层	矿体编号	矿体规模	占比/ %
银多金属矿	$w 34$	A7	薄层状延伸短	2.5
	$w 33$	A8	薄层状延伸短	2.5
	$w 32$	A1,A2,A3,A4,A5, A6,A9,A6-2	厚层状断续分布延长远	20
	$w 31$	A10,A10-2	薄层状延伸短	5
	$w 26$	A11,A12	薄层状延伸短	5
铅锌矿	$w 25$	A68,A70	薄层状延伸短	5
铅锌矿	$w 24$	A73,A75	薄层状延伸短	5
金多金属矿	$w 23$	51,51-1	薄层状延伸短	5
	$w 22$	52,52-1,52-2,52-3, 53,54,54-1,55,55-1, 56,57,3,3-1,3-2	厚层状断续分布延长远	35
	$w 21$	1,1-1,1-2,1-3	厚层状断续分布延长远	10
	$w 19$	Au-15	薄层状延伸短	2.5
	$w 18$	Au-5	薄层状延伸短	2.5

Table 3 Statistical table of ore-bearing property of different ore-bearing strata

矿种	赋矿地层	矿体编号	矿体规模	占比/ %
银多金属矿	$w 34$	A7	薄层状延伸短	2.5
	$w 33$	A8	薄层状延伸短	2.5
	$w 32$	A1,A2,A3,A4,A5, A6,A9,A6-2	厚层状断续分布延长远	20
	$w 31$	A10,A10-2	薄层状延伸短	5
	$w 26$	A11,A12	薄层状延伸短	5
铅锌矿	$w 25$	A68,A70	薄层状延伸短	5
铅锌矿	$w 24$	A73,A75	薄层状延伸短	5
金多金属矿	$w 23$	51,51-1	薄层状延伸短	5
	$w 22$	52,52-1,52-2,52-3, 53,54,54-1,55,55-1, 56,57,3,3-1,3-2	厚层状断续分布延长远	35
	$w 21$	1,1-1,1-2,1-3	厚层状断续分布延长远	10
	$w 19$	Au-15	薄层状延伸短	2.5
	$w 18$	Au-5	薄层状延伸短	2.5

Fig.8 The relationship between density body, low magnet, and ore-bearing strata

Table 4 Comprehensive information quantitative prediction model

信息类型	特征变量	描述	类型	值域	单元数量
地质实体信息量	$w 32$	歪头山组上部二岩段	二值型	0,1	39 317
	$w 22$	歪头山组中部二岩段	二值型	0,1	120 615
	$w 21$	歪头山组中部一岩段	二值型	0,1	130 777
	SC	碳质绢云石英片岩	二值型	0,1	103 537
	ηγ	隐伏酸性岩体	二值型	0,1	72 868
	F	断裂	二值型	0,1	147 807
Au找矿属性信息量	EH4_ρ_s	EH4_视电阻率	连续型	94.751 4,2 724.098 9	149 865
	磁化率	磁化率	连续型	0.004 7,0.011 8	418 361
	密度	密度	连续型	2.373 7,2.438 9	376 736
	Au	Ag品位插值	连续型	0.237 7,10.099 6	31 946
	As	As品位插值	连续型	0.016 1,0.625 8	31 652
	Sb	Sb品位插值	连续型	0.000 1,0.000 8	137 683
Ag找矿属性信息量	EH4_ρ_s	EH4_视电阻率	连续型	94.751 4,2 079.913 4	594 854
	磁化率	磁化率	连续型	0.004 7,0.019 2	157 569
	密度	视密度	连续型	2.413 9,2.489 0	487 082
	Ag	Ag品位插值	连续型	16.174 9,442.751 3	47 590
	As	As品位插值	连续型	0.016 1,0.625 8	31 652
	Sb	Sb品位插值	连续型	0.000 1,0.000 8	137 683

Table 4 Comprehensive information quantitative prediction model

信息类型	特征变量	描述	类型	值域	单元数量
地质实体信息量	$w 32$	歪头山组上部二岩段	二值型	0,1	39 317
	$w 22$	歪头山组中部二岩段	二值型	0,1	120 615
	$w 21$	歪头山组中部一岩段	二值型	0,1	130 777
	SC	碳质绢云石英片岩	二值型	0,1	103 537
	ηγ	隐伏酸性岩体	二值型	0,1	72 868
	F	断裂	二值型	0,1	147 807
Au找矿属性信息量	EH4_ρ_s	EH4_视电阻率	连续型	94.751 4,2 724.098 9	149 865
	磁化率	磁化率	连续型	0.004 7,0.011 8	418 361
	密度	密度	连续型	2.373 7,2.438 9	376 736
	Au	Ag品位插值	连续型	0.237 7,10.099 6	31 946
	As	As品位插值	连续型	0.016 1,0.625 8	31 652
	Sb	Sb品位插值	连续型	0.000 1,0.000 8	137 683
Ag找矿属性信息量	EH4_ρ_s	EH4_视电阻率	连续型	94.751 4,2 079.913 4	594 854
	磁化率	磁化率	连续型	0.004 7,0.019 2	157 569
	密度	视密度	连续型	2.413 9,2.489 0	487 082
	Ag	Ag品位插值	连续型	16.174 9,442.751 3	47 590
	As	As品位插值	连续型	0.016 1,0.625 8	31 652
	Sb	Sb品位插值	连续型	0.000 1,0.000 8	137 683

Table 5 Weight table of each predictive variable of Au evidence weight method

序号	Layer	W⁺	s(W⁺)	W^-	s(W^-)	C	s(C)	t-value
1	$w 22$	2.113 1	0.013 5	-0.869 0	0.016 6	2.982 1	0.021 4	139.264 8
2	EH4_ρ_s	1.875 8	0.014 0	-0.748 1	0.015 7	2.623 9	0.021 0	124.690 5
3	As	2.669 2	0.020 9	-0.291 2	0.012 1	2.960 4	0.024 1	122.697 1
4	SC	1.993 3	0.015 5	-0.557 7	0.014 1	2.551 0	0.021 0	121.621 9
5	F	1.774 0	0.014 4	-0.673 4	0.015 2	2.447 4	0.020 9	117.109 4
6	Sb	2.412 5	0.023 3	-0.216 8	0.011 6	2.629 3	0.026 0	100.935 0
7	Au	2.341 4	0.024 0	-0.199 7	0.011 5	2.541 1	0.026 7	95.290 7
8	密度	1.099 4	0.012 4	-0.973 3	0.019 3	2.072 7	0.022 9	90.606 0
9	$w 21$	1.429 5	0.017 9	-0.333 9	0.012 8	1.763 4	0.022 0	80.310 0
10	磁化率	0.747 3	0.014 1	-0.489 2	0.015 3	1.236 5	0.020 8	59.395 0
11	$w 32$	-5.458 9	1.000 0	0.025 2	0.010 4	-5.484 1	1.000 1	-5.483 7

Table 5 Weight table of each predictive variable of Au evidence weight method

序号	Layer	W⁺	s(W⁺)	W^-	s(W^-)	C	s(C)	t-value
1	$w 22$	2.113 1	0.013 5	-0.869 0	0.016 6	2.982 1	0.021 4	139.264 8
2	EH4_ρ_s	1.875 8	0.014 0	-0.748 1	0.015 7	2.623 9	0.021 0	124.690 5
3	As	2.669 2	0.020 9	-0.291 2	0.012 1	2.960 4	0.024 1	122.697 1
4	SC	1.993 3	0.015 5	-0.557 7	0.014 1	2.551 0	0.021 0	121.621 9
5	F	1.774 0	0.014 4	-0.673 4	0.015 2	2.447 4	0.020 9	117.109 4
6	Sb	2.412 5	0.023 3	-0.216 8	0.011 6	2.629 3	0.026 0	100.935 0
7	Au	2.341 4	0.024 0	-0.199 7	0.011 5	2.541 1	0.026 7	95.290 7
8	密度	1.099 4	0.012 4	-0.973 3	0.019 3	2.072 7	0.022 9	90.606 0
9	$w 21$	1.429 5	0.017 9	-0.333 9	0.012 8	1.763 4	0.022 0	80.310 0
10	磁化率	0.747 3	0.014 1	-0.489 2	0.015 3	1.236 5	0.020 8	59.395 0
11	$w 32$	-5.458 9	1.000 0	0.025 2	0.010 4	-5.484 1	1.000 1	-5.483 7

Table 6 Weight table of each predictive variable of Ag evidence weight method

序号	Layer	W⁺	s(W⁺)	W^-	s(W^-)	C	s(C)	t-value
1	$w 32$	2.582 8	0.031 6	-0.362 5	0.021 4	2.945 3	0.038 2	77.105 3
2	SC	1.989 7	0.025 9	-0.575 1	0.024 4	2.564 8	0.035 5	72.196 7
3	Ag	2.385 9	0.031 6	-0.355 0	0.021 4	2.740 9	0.038 2	71.779 8
4	Sb	1.164 8	0.021 6	-0.874 2	0.030 8	2.039 0	0.037 6	54.209 4
5	磁化率	1.369 3	0.028 4	-0.391 0	0.022 6	1.760 3	0.036 3	48.495 5
6	密度	0.913 1	0.020 2	-1.100 5	0.036 8	2.013 7	0.042 0	47.931 1
7	As	2.114 1	0.044 1	-0.158 2	0.019 3	2.272 3	0.048 2	47.186 2
8	F	1.106 3	0.033 3	-0.233 7	0.020 9	1.340 0	0.039 3	34.063 9
9	EH4_ρ_s	1.247 2	0.038 9	-0.171 3	0.019 9	1.418 5	0.043 7	32.482 2
10	$w 22$	-4.495 2	0.577 4	0.086 6	0.017 7	-4.581 8	0.577 6	-7.932 1
11	$w 21$	-1.402 8	0.128 1	0.061 3	0.017 9	-1.464 2	0.129 3	-11.323 0

Table 6 Weight table of each predictive variable of Ag evidence weight method

序号	Layer	W⁺	s(W⁺)	W^-	s(W^-)	C	s(C)	t-value
1	$w 32$	2.582 8	0.031 6	-0.362 5	0.021 4	2.945 3	0.038 2	77.105 3
2	SC	1.989 7	0.025 9	-0.575 1	0.024 4	2.564 8	0.035 5	72.196 7
3	Ag	2.385 9	0.031 6	-0.355 0	0.021 4	2.740 9	0.038 2	71.779 8
4	Sb	1.164 8	0.021 6	-0.874 2	0.030 8	2.039 0	0.037 6	54.209 4
5	磁化率	1.369 3	0.028 4	-0.391 0	0.022 6	1.760 3	0.036 3	48.495 5
6	密度	0.913 1	0.020 2	-1.100 5	0.036 8	2.013 7	0.042 0	47.931 1
7	As	2.114 1	0.044 1	-0.158 2	0.019 3	2.272 3	0.048 2	47.186 2
8	F	1.106 3	0.033 3	-0.233 7	0.020 9	1.340 0	0.039 3	34.063 9
9	EH4_ρ_s	1.247 2	0.038 9	-0.171 3	0.019 9	1.418 5	0.043 7	32.482 2
10	$w 22$	-4.495 2	0.577 4	0.086 6	0.017 7	-4.581 8	0.577 6	-7.932 1
11	$w 21$	-1.402 8	0.128 1	0.061 3	0.017 9	-1.464 2	0.129 3	-11.323 0

Fig.9 Fractal piecewise fitting line of Au, Ag evidence weight method

Fig.10 The predicts result of Au, Ag evidence weight method. a—Au prediction model; b—Ag prediction model.

Fig.11 Delineation of prospecting targets

Fig.12 Target area drilling verification results map

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