智能矿山大数据的地学信息挖掘与知识发现:以河南上房沟钼(铁)5G+矿山为例

doi:10.13745/j.esf.sf.2022.2.85

地学前缘 ›› 2023, Vol. 30 ›› Issue (4): 317-334.DOI: 10.13745/j.esf.sf.2022.2.85

智能矿山大数据的地学信息挖掘与知识发现:以河南上房沟钼(铁)5G⁺矿山为例

王洛锋¹(), 王功文²^,³^,⁴^,^*(), 许文辉¹, 徐森民¹, 何亚清¹, 王春毅¹, 杨涛¹, 周晓将¹, 黄蕾蕾², 左玲², 牟妮妮², 曹毅², 刘志飞², 常瑜琳²

1.洛阳栾川钼业集团股份有限公司, 河南洛阳 471500
2.中国地质大学(北京) 地球科学与资源学院, 北京 100083
3.北京市国土资源信息开发研究重点实验室, 北京 100083
4.自然资源部战略性金属矿产找矿理论与技术重点实验室, 北京 100083

收稿日期:2022-06-13 修回日期:2022-06-30 出版日期:2023-07-25 发布日期:2023-07-07
通讯作者: *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-环境定量预测评价的科研与教学工作。E⁃mail: gwwang@cugb.edu.cn
作者简介:王洛锋(1983-),男,高级工程师,主要从事矿山开采、工程爆破和智能矿山研究等。E-mail: wangluofeng1@126.com
基金资助:
洛阳栾川钼矿业集团项目“上房沟钼矿床矿物高光谱勘查与三维建模研究”(2021-2022);中国地质大学(北京)地质调查成果转化基金资助项目(2020-2021);中国地质大学(北京)地质调查成果转化基金资助项目(42932019022)

Intelligent geoscience information mining and knowledge discovery using big data analytics: A case study of the Shangfanggou Mo (Fe) mine in Henan Province

WANG Luofeng¹(), WANG Gongwen²^,³^,⁴^,^*(), XU Wenhui¹, XU Senmin¹, HE Yaqing¹, WANG Chunyi¹, YANG Tao¹, ZHOU Xiaojiang¹, HUANG Leilei², ZUO Ling², MOU Nini², CAO Yi², LIU Zhifei², CHANG Yulin²

1. China Molybdenum Co., Ltd., Luoyang 471500, China
2. School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
3. Beijing Key Laboratory of Land and Resources Information Research and Development, Beijing 100083, China
4. MNR Key Laboratory for Exploration Theory & Technology of Critical Mineral Resources, China University of Geosciences(Beijing), Beijing 100083, China

Received:2022-06-13 Revised:2022-06-30 Online:2023-07-25 Published:2023-07-07

摘要/Abstract

摘要：

21世纪工业4.0促进了智能矿山诞生,智能矿山的地质、勘探、采矿、选矿、环境、测绘等多学科数据集构成了矿山大数据,从而促进了地球科学的数字化、信息化和智能化迅速发展。本文以河南上房沟钼(铁)矿床5G⁺智能矿山为例,开展矿山大数据的地学信息挖掘,以凸显新工科多学科融合研究,取得了创新性成果与一系列新的地学知识发现。具体内容概括如下:(1)根据“斑岩-夕卡岩”矿床理论和找矿矿物学方法,利用钻孔数据集和露采场大比例尺填图及镜下鉴定分析,查明并构建了矿区主矿种与潜在矿种的时空三维模型,新发现了NE向构造赋矿地段、贯入式赋矿地段;(2)利用无人机遥感与地面高光谱短波红外和长波红外技术,识别并建立了矿区20多种主要蚀变矿物并构建了三维多参数矿物模型;(3)运用地球化学XRF测量和微区原位测量技术,建立了高光谱匹配的样品数据集,研发了矿区岩矿石有用和有害元素双矩阵制图软件,实现了传统地质统计学(高斯模拟、克里格插值)与机器学习(深度学习)关联的数学建模,复原并查明了2021年3-4月选矿回收率偏低的配矿矿石矿物组合及其缘由;(4)运用矿区选矿工艺矿物学的生产与实验采选矿的多期次多类型数据集(季-月-日的岩粉、泥粉、精矿、尾矿等,>1 800),研发岩粉与矿粉测试技术与分析方法,查明了上房沟钼矿的难选矿石类型及其有害矿物种类。研究结果表明,矿山多元多类型的数据集具有大数据“5V”(volume, variety, velocity, veracity, value)特征,准确管控矿山大数据的动态关联测量、分析与快速、高效评价有利于矿山智能决策和经济效益提高(回收率)。其中,高精度的多参数三维建模不仅能够深层次挖掘岩矿石的地质、构造、蚀变、矿化等信息模型,核实储量/资源量,还能满足第四代工业5G⁺矿山的实时矿业(real-time-mining)发展的四维管控需求,例如三维空间可视化的地质矿产资源预测与评价及增储、虚拟仿真式的“年-季-月-日”动态配矿采矿、数字孪生的实时选矿等。本项研究成果为智能矿山的地质矿产深层次地学研究提供了借鉴和参考。

关键词: 矿山大数据, 高光谱, XRF, 地球化学原位分析, 地学信息挖掘, 知识发现, 上房沟Mo多金属矿床

Abstract:

Industry 4.0 of the 21^st century has given birth to smart mines. The multidisciplinary datasets of smart mines-such as geology, exploration, mining, geometallurgy, environment and survey/map datasets-constitute big data of mines, and they play an important role for the rapid advancements of geoscience in areas of geoscience digitization and application of information/Al technology in geoscience. Taking the Shangfanggou Mo (Fe) mine, a 5G⁺ smart mine, in Henan Province as an example, using big data of mines, this paper carried out geoscience information mining to highlight emerging engineering research with integrated multidisciplinary approach. Innovative results and geological knowledge discoveries from this study are summarized as follows: (1) According to theories on porphyry-associated skarns and mineralogical approach to minera resources prospecting, using borehole datasets and large-scale open-pit mapping and microscopic identification analysis, a 3D temporo-spatial model of the identified key minerals and predicted minerals in the study area was established, and a NE trending ore-bearing fault section and a penetration-type ore-bearing section were discovered. (2) Using UAV remote sensing and ground hyperspectral short-wave/long-wave infrared techniques, more than 20 types of key altered minerals in the study area were delineated, and a 3D multi-parameter mineral model was constructed. (3) Using geochemical techniques such as XRF and in-situ microscopy, a rock dataset with matching hyperspectral interpretation was established, and a dual-matrix mapping software for useful/harmful elements of rocks/ores in the study area was developed. In addition, mathematical modeling combining traditional geostatistics (gauss simulation, kriging interpolation) with machine learning (deep learning) was realized, and the composition of ore blends used between March-April 2021 was identified and the cause of the resulting low recovery rate was clarified. (4) Based on process mineralogy practice in the study area, multi-stage, multi-type mineral processing datasets (>1800 data on quarterly/monthly/daily processing of rock powder, mud powder, concentrate, tailings, etc.) were used to develop rock/mineral powder testing techniques and analysis methods, and the types of refractory ores and harmful minerals in the Shangfanggou Mo mine were identified. The multivariate, multi-type datasets of mines have the “5V” (volume, variety, velocity, veracity, value) characteristics of big data. The accurate management control of dynamic correlation measurement/analysis and rapid/efficient evaluation of big data of mines is conducive to intelligent mining decision-making and improvement of economic benefit (recovery rate). Among them, high-precision multi-parameter 3D modeling can be applied not only to deep mining of geological, structural, alteration and mineralization information models of rocks/ores as well as reserve/resources verification, but also to facilitating 4D control on real-time mining of fourth generation industrial 5G⁺ mines, such as 3D visualization of geological and mineral resources prediction/evaluation/storage expansion, virtual simulation of “year-quarter-month-day” dynamic ore blending and mining, and real-time digital twin for mine beneficiation. The research results provide a reference for in-depth geoscience research on mineral exploration and mineral resources assessment in smart mines.

Key words: mine big data, hyperspectral, XRF, in-situ geochemical analysis, geoscience information mining, knowledge discovery, Shangfanggou Mo polymetallic deposit

中图分类号:

P628
P618.65

王洛锋, 王功文, 许文辉, 徐森民, 何亚清, 王春毅, 杨涛, 周晓将, 黄蕾蕾, 左玲, 牟妮妮, 曹毅, 刘志飞, 常瑜琳. 智能矿山大数据的地学信息挖掘与知识发现:以河南上房沟钼(铁)5G⁺矿山为例[J]. 地学前缘, 2023, 30(4): 317-334.

WANG Luofeng, WANG Gongwen, XU Wenhui, XU Senmin, HE Yaqing, WANG Chunyi, YANG Tao, ZHOU Xiaojiang, HUANG Leilei, ZUO Ling, MOU Nini, CAO Yi, LIU Zhifei, CHANG Yulin. Intelligent geoscience information mining and knowledge discovery using big data analytics: A case study of the Shangfanggou Mo (Fe) mine in Henan Province[J]. Earth Science Frontiers, 2023, 30(4): 317-334.

图/表 15

图1 上房沟钼铁矿床地质简图 (A,B引自文献[11];C据文献[11-12]修改) 1-第四系;2-煤窑沟组上段;3-煤窑沟组中段;4-煤窑沟组下段;5-花岗斑岩;6-花岗斑岩岩墙;7-辉长岩;8-辉绿岩;9-压扭性断层;10-张扭性断层;11-地质界线;12-蚀变带界线;13-磁铁-透闪透辉石化带;14-蛇纹石化带;15-金云母-阳起石化带;16-强硅化带。

Fig.1 Simplified geological maps showing the location of the study area (a, adapted from [11]), location of the Shangfanggou Mo (Fe) mine (b, adapted from [11]), and tectono-geological setting of the Shangfanggou deposit (c, modified after [11-12])

图2 上房沟矿区的无人机多期遥感(2020, 2021, 2022年)影像与雷达点云图

Fig.2 UAV multi-phase remote sensing images (years 2020, 2021, 2022) and radar point cloud maps of Shangfanggou mine

图3 研究区上房无人机倾斜测量三维影像(3D surface)与RTK(cm级)定点样品分布图 A-夕卡岩型铁矿采矿地段;B-辉钼矿采矿地段;C-富辉钼矿矿石样品;D-氧化钼难选矿石样品。

Fig.3 3D surface images of minding areas (upper panel) ), UAV captured image of the study area showing distribution of sampling points (RTK positioning with cm resolution), and ore specimen (lower panel)

图4 A-基于108个钻孔数据库的岩性标样三维矿物建模;B-研究区岩矿石标样及图层样品位置图;C-短波红外采场位置图

Fig.4 (A) 3D mineral content modeling of ore standard samples for lithofacies classification based on 108 borehole databases, (B) sampling locations of rock/ore standard samples and layer samples in the study area, and (C) short-wave infrared stope location map.

图5 (a)短波红外岩粉样品测试分析;(b)长波红外岩粉样品测试分析

Fig.5 Relative percentages of TSA minerals in rock powder samples by short-wave (a) and long-wave (b) infrared spectroscopy

图6 基于GOCAD软件显示建模的研究区三维地质建模流程

Fig.6 3D geological modeling of the study area using GOCAD software

图7 洛阳栾川钼矿业富川上房矿山中5G+控室三维地质体管控:钼的氧化矿(主矿体)(黄色)、2021年岩矿石采矿(爆堆)矿块(蓝色)、围岩地层(灰色)和样品采样点三维多元信息模型

Fig.7 3D control of geological bodies at the 5G+ control room, Shangfanggou mine, Luoyang Luanchuan Molybdenum Mining Co., Ltd., showing oxidized Mo ore (main ore body) (yellow), 2021 mined (blasted) ore block (blue), wall-rock (gray), and 3D multivariate information model of sampling points

图8 上房沟钼矿体三维克里格插值模型

Fig.8 3D Kriging interpolation model of the Shangfanggou Mo deposit

表1 上房沟矿床岩矿石粉末样品的TIR光谱与XRF分析数据集(部分)

Table 1 Mineral composition (by TIR, XRF) dataset (part) of rock/ore powder samples from Shangfanggou deposit

编号	各主成分矿物TIR(热红外)光谱分析结果						w(Fe)/%	w(Mo)/%	w(W)/10^-6
编号	第一主成分	含量	第二主成分	含量	第三主成分	含量	w(Fe)/%	w(Mo)/%	w(W)/10^-6
02-11-1	石英	0.631	白云石	0.369			4.019 8	0.075 2	75
02-11-2	石英	0.658	蛇纹石	0.342			3.737 5	0.101 6	67
02-11-3	石英	0.622	蛇纹石	0.378			4.412 4	0.120 4	106
02-12-1	绿脱石	0.397	石英	0.319	微斜长石	0.284	4.579 4	0.141 9	73
02-12-2	绿脱石	0.422	石英	0.300	微斜长石	0.278	4.171 5	0.124 2	91
02-12-3	绿脱石	0.462	石英	0.291	微斜长石	0.247	5.220 6	0.139 8	117
02-13-1	绿脱石	0.462	石英	0.275	微斜长石	0.262	5.013 5	0.119 3	89
02-13-2	绿脱石	0.409	石英	0.308	微斜长石	0.283	4.734 5	0.130 7	82
02-14-1	拉长石	0.361	石英	0.339	滑石	0.3	4.237 8	0.094 7	76
02-14-3	绿脱石	0.382	石英	0.339	微斜长石	0.279	4.039 0	0.112 3	88
02-14-2	绿脱石	0.406	石英	0.308	微斜长石	0.287	6.733 2	0.133 0	113
02-15-1	绿脱石	0.373	石英	0.332	微斜长石	0.294	6.733 2	0.133 0	113
02-15-2	石英	0.573	蛇纹石	0.427			6.733 2	0.133 0	113
03-31-1	绿脱石	0.569	蛇纹石	0.261	石英	0.17	9.198 9	0.129 6	99
03-31-2	滑石	0.459	角闪石	0.294	钠钙长石	0.248	10.623 5	0.124 4	140
03-31-3	滑石	0.355	角闪石	0.349	拉长石	0.297	10.623 5	0.124 4	140
04-01-1	滑石	0.484	角闪石	0.355	石英	0.161	10.724 8	0.127 6	137
04-01-2	滑石	0.424	角闪石	0.423	石英	0.153	10.557 7	0.125 8	117
04-01-3	滑石	0.519	角闪石	0.306	石英	0.175	10.129 6	0.134 0	124
04-02-1	滑石	0.463	角闪石	0.332	钠钙长石	0.204	11.473 4	0.130 1	121

图9 上房沟矿山2021年1、2月粉末样品热图样品元素分类

Fig.9 Heap map comparing elemental composition of powder samples from Shangfanggou mine between January-February 2021

图10 上房沟矿山2021年3、4月粉末难回收(回收率低)样品热图样分类

Fig.10 Heap map comparing elemental composition of low-recovery powder samples from Shangfanggou mine between March-April 2021

图11 上房沟矿床的高滑石中氧化高铁高钼矿石选矿厂试验分析的地学信息挖掘及地学认知过程分析

Fig.11 Geological information mining and decision making process based on ore processing result for high-talc, medium-oxidation, high-Fe, high-Mo ore from the Shangfanggou deposit

图12 基于斑岩-夕卡岩成矿理论与三维地质体建模的知识发现:矿区富铁地段与夕卡岩金云母关联的三维地质模型

Fig.12 Knowledge discovery-based 3D geological models of the Fe-rich section (left) and phlogopite-bearing skarn (right) in the mining area showing their associative relationship

图13 上房沟Fe矿体三维克里格插值模型与断层三维模型

Fig.13 3D Kriging interpolation model and fault model of the Shangfanggou Fe deposit

图14 基于勘探蚀变信息、采矿配矿信息、选矿有害矿物信息综合构建的蚀变矿物不利程度分布模型

Fig.14 Hazard-level distribution model of altered minerals based on mineral alteration status and information on mining, mineral processing and harmful minerals

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智能矿山大数据的地学信息挖掘与知识发现:以河南上房沟钼(铁)5G⁺矿山为例

Intelligent geoscience information mining and knowledge discovery using big data analytics: A case study of the Shangfanggou Mo (Fe) mine in Henan Province

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