栾川矿集区地学大数据挖掘和三维/四维建模的资源-环境联合预测与定量评价

doi:10.13745/j.esf.sf.2021.1.1

地学前缘 ›› 2021, Vol. 28 ›› Issue (3): 139-155.DOI: 10.13745/j.esf.sf.2021.1.1

• 三维地质建模与隐伏矿预测评价 • 上一篇下一篇

栾川矿集区地学大数据挖掘和三维/四维建模的资源-环境联合预测与定量评价

王功文¹(), 张寿庭¹, 燕长海², 庞振山³, 王宏伟⁴, 冯占奎⁵, 董宏⁶, 程红涛⁷, 何亚清⁸, 李瑞喜¹, 张智强¹, 黄蕾蕾¹, 郭娜娜⁴

1.中国地质大学(北京) 地球科学与资源学院, 北京 100083
2.河南省金属矿产成矿地质过程与资源利用重点实验室, 河南郑州 450001
3.中国地质调查局发展研究中心, 北京 100083
4.河南省栾川县自然资源局, 河南洛阳 471500
5.栾川中洲九鼎矿业有限公司, 河南洛阳 471500
6.中国地质矿业有限公司, 北京 100029
7.河南众鑫矿业有限责任公司, 河南洛阳 471500
8.河南省洛阳栾川钼业集团股份有限公司, 河南洛阳 471500

收稿日期:2021-01-02 修回日期:2021-01-12 出版日期:2021-05-20 发布日期:2021-05-23
作者简介:王功文(1972—),男,教授,博士生导师,从事三维地质建模与地学信息集成的资源定量预测评价科研工作。E-mail: gwwang@cugb.edu.cn
基金资助:
国家“十三五”重点研发计划项目(2016YFC0600509);国家“十三五”重点研发计划项目(2016YFC0600107);国家“十三五”重点研发计划项目(2016YFC0600108);中国地质大学(北京)地质调查成果转化基金资助项目(2020—2021)

Resource-environmental joint forecasting in the Luanchuan mining district, China through big data mining and 3D/4D modeling

WANG Gongwen¹(), ZHANG Shouting¹, YAN Changhai², PANG Zhenshan³, WANG Hongwei⁴, FENG Zhankui⁵, DONG Hong⁶, CHENG Hongtao⁷, HE Yaqing⁸, LI Ruixi¹, ZHANG Zhiqiang¹, HUANG Leilei¹, GUO Nana⁴

1. School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China
2. Key Laboratory of Metallogenetic Processes and Resource Utilization, Zhengzhou 450001, China
3. China Geological Survey Development Research Center, Beijing 100083, China
4. Luanchuan County Natural Resources Bureau, Luoyang 471500, China
5. Luanchuan Zhongzhou Jiuding Mining Industry Co., Ltd., Luoyang 471500, China
6. China Geology & Mining Co., Ltd., Beijing 100029, China
7. Henan Zhongxin Mining Co., Ltd., Luoyang 471500, China
8. Henan China Molybdenum, Luoyang 471500, China

Received:2021-01-02 Revised:2021-01-12 Online:2021-05-20 Published:2021-05-23

摘要/Abstract

摘要：

21世纪地球科学的“第四范式”与第四工业时代以及5G+智能通信为矿业开发与环境防护的关联研究提供了新契机。以地球动力学背景、成矿过程、定量评价“三位一体”的地学理论为基础,以栾川矿集区为例,运用地学大数据(多维多尺度的地质、地球物理、地球化学、高光谱与高分辨率遥感(多时相)以及实时矿业等数据)的深层次人工智能挖掘和三维/四维多学科多参数多尺度建模技术平台,开展矿集区至矿床多尺度的三维地质模型、成矿过程模型和定量勘查模型构建及其资源的定量预测评价,旨在实现数字矿山的高精度三维地质(岩石、构造、水文、土壤等)环境保护和资源综合开发利用的动态评价,为研究区矿产资源与矿山环境可持续发展提供科学依据。研究结果概述如下:(1)地球科学大数据关联矿集区资源预测评价。利用三维地质建模、地质-地球物理正反演解译、地球化学与遥感等地学数据深层次挖掘,结合自主研发GeoCube2.0集成软件,实现了栾川矿集区(500 km²,深部2.5 km)的深部靶区优选和矿产资源综合评价,Mo资源量650万t,W资源量150万t, Pb-Zn-Ag累计具有500万t资源量。(2)地质、矿床与勘探的三维地学建模关联矿山环境。南泥湖—三道庄—上房矿山露采场与骆驼山深部巷道勘探与采矿的资料表明,区域NW向的斑岩夕卡岩型矿床与矿体与地下水空间关联度不高,而成矿期后通常具有张性或张扭性特征的NE向断裂是地下水运移的通道;在NW向Pb-Zn矿床地段具有显著淋滤特征的次生金属矿产出,浅表的氧化铅锌矿与锰铁矿伴生孔雀石化、铅华等水蚀作用;高海拔Pb-Zn矿区且NE向断裂构造发育的冷水、百炉沟地段存在地下水污染风险。(3)智慧矿山构建关联资源环境评价与决策。在大型矿山建立三维地质模型并关联矿区古采洞、露采场与深部巷道工程,实现矿业合理定位和可持续发展;利用高光谱数据库构建三维有用、有害元素模型实现勘探、采矿与选矿矿物学关联以便于有害元素(As、Sb、Hg等)的回收或尾矿处理;利用高分辨率Worldview2影像判别重要尾矿库的废水、矿渣泥浆含铁染分布,以便于防护地表径流水、土壤污染等。

关键词: 大数据挖掘, 三维/四维建模, 定量预测评价, 资源环境, 智慧矿山, 栾川矿集区

Abstract:

The “fourth paradigm” of the 21 ^st century Earth Science, the fourth industrial era and the 5G+ intelligent communication provide mineral researchers with a new opportunity to study the relationship between mining development and environmental protection. This study is based on the geoscientific theory that emphasizes the connections between geodynamic background, metallogenic process and quantitative assessment. Taking the Luanchuan mining district as an example, using geoscience big data (multi-dimensional, multi-scale geological, geophysical, geochemical, multi-temporal hyperspectral/high-resolution remote sensing, real-time mining data) platform, including deep artificial intelligence mining technology as well as 3D/4D multi-disciplinary, multi-parameter, multi-scale modeling technology, we constructed a 3D geological model, a metallogenic process model and a quantitative exploration model at the district and deposit scales, and performed quantitative prediction and assessment of the related mineral resources. The aim was to achieve dynamic assessment of high-precision 3D geo-environmental (rock, structure, hydrology, soil, etc.) protection and comprehensive development and utilization of resources in digital mines, so as to provide a scientific basis for the sustainable mineral resource development in the study area. The research results are summarized in three main aspects: (1) Application of geoscience big data in resource prediction and assessment of district. By mining geoscience big data such as 3D geological modeling, geological and geophysical forward/backward interpretation, geochemical and remote sensing data mining, etc., with use of the self-developed GeoCube2.0 software, deep target optimization and comprehensive mineral resources assessment were achieved for the Luanchuan mining district (500 km² in area, 2.5 km deep). The predicted Mo, W and Pb-Zn-Ag resources in the mining district were 6.5, 1.5 and 5 million tons, respectively. (2) Three-dimensional geologic modeling of ore deposit and mineral exploration related to mining environment. Field data collected from the Nannihu-Sandaozhuang-Shangfang open pits and the Luotuoshan deep channel mining zones show that the spatial correlation between the NW-trending porphyry-skarn deposit/orebody and groundwater space is not high, and the NE-trending faults—usually tensional or tenso-torsional in the post-metallogenic period—are the pathway for groundwater migration. In the NW-trending Pb-Zn deposit, there are significant secondary metalloid leaching, where shallow Pb-Zn oxides and Mn-Fe minerals are associated with malachite and bloom from water erosion. There is a risk of groundwater pollution in the high-altitude Pb-Zn mining zone as well as in the Lengshui and Bailugou deposits, where NE-trending faults are developed outside of the porphyry-skarn Mo(W) deposits in the study area. (3) Geoscience model construction in intelligent digital mines for resource-environmental joint assessment and planning. A 3D geological model, with relevance to ancient mining caves, open pits, and deep laneways in the mining area, was established for large mines to provide guidance for the mining industry toward sustainable resource development. Using hyperspectral database, 3D models for both useful and harmful mineral elements were constructed to unite the mineral exploration, mining and processing processes in facilitating the recovery of harmful elements (As, Sb, Hg, etc.). And high resolution WorldView-2 images were used to gauge the distribution of Fe-containing pollutants in waste water and slag slurry of significant tailings reservoirs in protecting against surface runoff and soil pollution.

Key words: big data mining, 3D/4D modeling, quantitative prediction and assessment, resource and environment, intelligent mine, Luanchuan mining district

中图分类号:

P612

王功文, 张寿庭, 燕长海, 庞振山, 王宏伟, 冯占奎, 董宏, 程红涛, 何亚清, 李瑞喜, 张智强, 黄蕾蕾, 郭娜娜. 栾川矿集区地学大数据挖掘和三维/四维建模的资源-环境联合预测与定量评价[J]. 地学前缘, 2021, 28(3): 139-155.

WANG Gongwen, ZHANG Shouting, YAN Changhai, PANG Zhenshan, WANG Hongwei, FENG Zhankui, DONG Hong, CHENG Hongtao, HE Yaqing, LI Ruixi, ZHANG Zhiqiang, HUANG Leilei, GUO Nana. Resource-environmental joint forecasting in the Luanchuan mining district, China through big data mining and 3D/4D modeling[J]. Earth Science Frontiers, 2021, 28(3): 139-155.

图/表 15

图1 地区地质简图、栾川矿集区大地构造位置与三维地质模型(500 km2,垂向2.5 km)

Fig.1 Simplified geological map (a), tectonic sketch map (b), and three dimensional geological model (c) of the Luanchuan mining district (500 km2 in area, 2.5 km in vertical depth)

图2 栾川矿集区遥感影像(ETM+342)与NW向构造模型(垂向2.5 km)

Fig.2 Remote sensing image (ETM+342) and NW-trending structural model (2.5 km in vertical depth) of the Luanchuan mining district

图3 栾川矿集区大数据挖掘与三维建模的资源预测评价流程图

Fig.3 Flowchart of resources prediction and evaluation based on big data mining and 3D modeling for the Luanchuan mining district

图4 栾川1:25 000重磁综合解译燕山期岩体与CSAMT剖面交互验证

Fig.4 Cross-validation of 1:25000 gravity and magnetic interpretation of intrusive bodies from the Yanshan period as compared to CSAMT image for the Luanchuan area

图5 栾川1:25 000重磁综合解译燕山期岩体与三维NW、NE向断裂构造

Fig.5 1:25000 gravity and magnetic interpretation of intrusive bodies from the Yanshan period and 3D structural models of the NW and NE-trending faults for the Luanchuan district

图6 栾川三维地质模型(部分)与MT剖面关联解译

Fig.6 Interpretation of the correlation between the 3D geological model (a portion is shown) of the Luanchuan area and the MT section

图7 栾川矿集区地质(实测)-浅源地震-CSAMT-重磁反演-构造裂隙化探综合找矿模型(据文献[25]修改)

Fig.7 Composite diagram showing the integrated prospecting model involving in situ geology, shallow source seismology, CSAMT, gravity-magnetic inversion, and tectono-geochemical exploration for the Luanchuan mining district. Modified after [25].

图8 基于Flac3D的栾川石宝沟矿区的三维侵入岩体动态模拟结果(引自文献[7])

Fig.8 Dynamic simulation results of 3D intrusive rock mass in the Shibaogou mining field, Luanchuan area by Flac 3D. Adapted from [7].

图9 基于成矿动力学实验分析的栾川矿集区推覆构造几何模型(引自文献[7])

Fig.9 Geometric modeling of the nappe structure of the Luanchuan mining district by experimental metallogenic dynamics analysis. Adapted from [7].

图10 基于Worldview-2 高分辨率遥感影像(0.5 m)栾川南泥湖Mo矿田特征

Fig.10 Worldview-2 high resolution (0.5 m) remote sensing image showing the main features of the Nannihu Mo orefield, Luanchuan area

图11 栾川南泥湖Mo矿田三维地质、矿床、构造与岩体模型

Fig.11 Results of 3D geological, depositional and fault/intrusive body modeling of the Nannihu Mo orefield, Luanchuan area

图12 基于GeoCube2.0软件集成的栾川Mo与Pb-Zn靶区优选模型

Fig.12 Results of priority targeting of Mo and Pb-Zn mining zones of the Luanchuan area using GeoCube2.0 software

表1 基于GeoCube2.0软件集成的栾川Mo与Pb-Zn矿区外围主要靶区优选

Table 1 Evaluation summary of main targeting areas surrounding the Mo and Pb-Zn mining zones of the Luanchuan area using GeoCube2.0 software

级别	地段-编号	靶区	坐标中心位置 (X,Y,Z)	靶区概述	矿种	估算资源量(含储量) /万t
A	A1	大坪	553 400,3 748 700,1 040	大坪岩体周边为浅隐伏地段,预测地表以下100 m见矿。地表出露岩体、Mo多金属矿脉,蚀变强,断裂构造与褶皱发育,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 30 Pb-Zn 120
	A2	碾道沟	549 100,3 751 300,920	石宝沟岩体东北部为浅隐伏地段,预测地表以下200 m见矿,有隐伏岩体(石宝沟岩体北部侧伏),NE向构造控矿显著,物探异常明显估算域:热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 30 Pb-Zn110
	A3	扎子沟	547 500,3 750 400,1 100	石宝沟岩体西北部为浅隐伏地段,预测地表以下200 m见矿,有隐伏岩体(石宝沟岩体NE向侧伏),NE向构造控矿显著,物探异常明显估算域:热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 40 Pb-Zn 120
	A4	大南沟	542 200,3 753 000,1 340	上房西南为隐伏矿段,预测靶区深达海拔500 m标高,有隐伏岩体,NE向构造控矿显著,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 10 Pb-Zn 100
	A5	冷水西	541 300,3 758 300,920	冷水西部为隐伏矿段,预测靶区深达海拔600 m标高,有隐伏岩体,NE向构造控矿显著,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 10 Pb-Zn 100
	A6	火神庙东	532 400,3 757 500,560	火神庙东为隐伏矿段,预测靶区深达海拔300 m标高以下,分布有隐伏中酸性岩体和基性岩体,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 30 Pb-Zn 110
	A7	鱼库北	544 900,3 749 400,860	东鱼库北部为浅隐伏地段,预测靶区地表以下200 m见矿,分布有隐伏中酸性岩体和基性岩体,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 40 Pb-Zn 100
B	B1	黄背岭西、西北	540 990,3 750 800,1 250	黄背岭西、西北部为Pb-Zn预测找矿靶区,西北部有Mo远景区。分布有隐伏岩体,物探异常明显估算域:斑岩型-夕卡岩型Mo矿、热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 40 Pb-Zn 80
	B2	红洞沟东南部	539 800,3 747 400,1 100	红洞沟东南为Pb-Zn预测找矿靶区。构造控矿特征显著;陶湾群地层内分布有酸性小岩体,物探异常明显估算域:热液脉型Pb-Zn矿	Pb-Zn	Pb-Zn 70
	B3	白沙洞东南	554 500,3 755 300,980	白沙洞东南为Pb-Zn预测找矿靶区。NW、NE向构造控矿特征显著;出露官道口群地层,深部可能有燕山期岩体或岩脉,物探异常明显估算域:热液脉型Pb-Zn矿	Pb-Zn	Pb-Zn 60

表1 基于GeoCube2.0软件集成的栾川Mo与Pb-Zn矿区外围主要靶区优选

Table 1 Evaluation summary of main targeting areas surrounding the Mo and Pb-Zn mining zones of the Luanchuan area using GeoCube2.0 software

级别	地段-编号	靶区	坐标中心位置 (X,Y,Z)	靶区概述	矿种	估算资源量(含储量) /万t
A	A1	大坪	553 400,3 748 700,1 040	大坪岩体周边为浅隐伏地段,预测地表以下100 m见矿。地表出露岩体、Mo多金属矿脉,蚀变强,断裂构造与褶皱发育,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 30 Pb-Zn 120
	A2	碾道沟	549 100,3 751 300,920	石宝沟岩体东北部为浅隐伏地段,预测地表以下200 m见矿,有隐伏岩体(石宝沟岩体北部侧伏),NE向构造控矿显著,物探异常明显估算域:热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 30 Pb-Zn110
	A3	扎子沟	547 500,3 750 400,1 100	石宝沟岩体西北部为浅隐伏地段,预测地表以下200 m见矿,有隐伏岩体(石宝沟岩体NE向侧伏),NE向构造控矿显著,物探异常明显估算域:热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 40 Pb-Zn 120
	A4	大南沟	542 200,3 753 000,1 340	上房西南为隐伏矿段,预测靶区深达海拔500 m标高,有隐伏岩体,NE向构造控矿显著,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 10 Pb-Zn 100
	A5	冷水西	541 300,3 758 300,920	冷水西部为隐伏矿段,预测靶区深达海拔600 m标高,有隐伏岩体,NE向构造控矿显著,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 10 Pb-Zn 100
	A6	火神庙东	532 400,3 757 500,560	火神庙东为隐伏矿段,预测靶区深达海拔300 m标高以下,分布有隐伏中酸性岩体和基性岩体,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 30 Pb-Zn 110
	A7	鱼库北	544 900,3 749 400,860	东鱼库北部为浅隐伏地段,预测靶区地表以下200 m见矿,分布有隐伏中酸性岩体和基性岩体,物探异常明显估算域:斑岩-夕卡岩型Mo与热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 40 Pb-Zn 100
B	B1	黄背岭西、西北	540 990,3 750 800,1 250	黄背岭西、西北部为Pb-Zn预测找矿靶区,西北部有Mo远景区。分布有隐伏岩体,物探异常明显估算域:斑岩型-夕卡岩型Mo矿、热液脉型Pb-Zn矿	Pb-Zn、Mo	Mo 40 Pb-Zn 80
	B2	红洞沟东南部	539 800,3 747 400,1 100	红洞沟东南为Pb-Zn预测找矿靶区。构造控矿特征显著;陶湾群地层内分布有酸性小岩体,物探异常明显估算域:热液脉型Pb-Zn矿	Pb-Zn	Pb-Zn 70
	B3	白沙洞东南	554 500,3 755 300,980	白沙洞东南为Pb-Zn预测找矿靶区。NW、NE向构造控矿特征显著;出露官道口群地层,深部可能有燕山期岩体或岩脉,物探异常明显估算域:热液脉型Pb-Zn矿	Pb-Zn	Pb-Zn 60

图13 栾川赤土店高分辨率遥感与巷道工程关联的数字化矿山三维管控平台

Fig.13 Digital mine 3D platform based on high resolution remote sensing and channel engineering in the Chitudian deposit in the Luanchuan district

图14 赤土店三维西沟铅锌矿高精度勘探钻孔、矿体、采矿工程模型(位置见图10)

Fig.14 Three-dimensional model of high-precision exploration drilling, orebody and channel mining engineering for the Chitudian Xigou Pb-Zn mine (location see Fig.10)

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栾川矿集区地学大数据挖掘和三维/四维建模的资源-环境联合预测与定量评价

Resource-environmental joint forecasting in the Luanchuan mining district, China through big data mining and 3D/4D modeling

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