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3D Mineral Prospectivity Mapping Based on Deep Metallogenic Prediction Theory:A Case Study of the Lala Copper Mine,Sichuan,China

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Journal of Earth Science 2021年第2期32卷 348-357页

作者： keyan xiao Jie Xiang Mingjing Fan Yang Xu MNR Key Laboratory of Metallogeny and Mineral Resource Assessment Institute of Mineral ResourcesChinese Academy of Geological SciencesBeijing 100037China International Mining Research Center China Geological SurveyBeijing 100037China China Mining News China Geological SurveyBeijing 100037China Institute of Geological Survey China University of GeosciencesWuhan 430074China China University of Geosciences Beijing 100083China

With the decrease in surface and shallow ore deposits,mineral exploration has focused on deeply buried ore bodies,and large-scale metallogenic prediction presents new opportunities and *** paper adopts the predictive thinking method in this era of big data combined with specific research on the special exploration and exploitation of deep-earth *** basic theoretical models of large-scale deep mineralization prediction and evaluation are explored:mineral prediction geological model theory,multidisciplinary information correlation theory,mineral regional trend analysis theory,and mineral prediction geological differentiation *** main workflow of large-scale deep resource prediction in the digital and information age is summarized,including construction of ore prospecting models of metallogenic systems,multiscale 3 D geological modeling,and 3 D quantitative prediction of deep *** the Lala copper mine in Sichuan Province as an example,this paper carries out deep 3 D quantitative prediction of mineral resources and makes a positive contribution to the future prediction and evaluation of mineral resources.

关键词： deep mine resources prospectivity mapping 3D modeling quantitative evaluation Lala copper mine

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Geochemical Features and Metallogenic Prediction of the Caixiashan-Weiquan Area in the East Tianshan Region

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Acta Geologica Sinica(English Edition) 2012年第4期86卷 885-893页

作者： LU Lu ZHU Lixin xiao keyan MA Shengming YIN Jiangning XU Mingzuan Chinese Academy of Geological Sciences Institute of Mineral Resources Chinese Academy of Geological Sciences Institute of Geophysical and Geochemical Exploration CAGS Jiangsu Provincial Institute of Geological Prospecting Techniques

The Caixiashan-Weiquan area is an important ore concentration area in the eastern Tianshan metallogenic belt. Firstly, this paper studies geochemical features of 1564 samples of 1：200000 stream sediments of the Matoutan mapsheet, where the Caixiashan and Weiquan deposits are located. Processing, analysis and explanation of exploration geochemical data play an important role in the procedure of finding the ore, which are related to whether the measured elements content of geochemical samples can effectively guide the work of mineral exploration. As a highly nonlinear dynamical system, the neural network is more analogous to the human brains in terms of principles and features compared with conventional geochemical approaches. It can adapt itself to the environment, sum up laws, complete pattern recognition. Secondly, the authors used the Kohonen neural network to classify all samples based on 10 mineralization elements of stream sediment samples in order to determine possible mineral ores, reduce the scope of ore targets and study indicator elements of the ninth group of samples, which is the mostly closest to the deposit. The results show that the neural network can delineate metallogenic prospective areas and is effective in the discovery of deep geochemical information.

关键词： Regional exploration geochemistry Pb-Zn deposits neural network metallogenic prediction

Single-element Anomaly Mapping from Stream Sediment Geochemical Landscapes Aided by Digital Terrain Analysis

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Acta Geologica Sinica(English Edition) 2023年第1期97卷 149-162页

作者： XIANG Jie XIA Peng xiao keyan Emmanuel John M.CARRANZA CHEN Jianping International Mining Research Center China Geological SurveyBeijing 100037China China Mining News China Geological SurveyBeijing 100037China Institute of Advanced Studies China University of GeosciencesWuhan 430074China MNR Key Laboratory of Metallogeny and Mineral Resource Assessment Institute of Mineral ResourcesChinese Academy of Geological SciencesBeijing 100037China Department of Geology University of the Free StateBloemfonteinSouth Africa School of Earth Sciences and Resources China University of GeosciencesBeijing 100083China

The identification of anomalies within stream sediment geochemical data is one of the fastest developing areas in mineral *** various means used to achieve this objective make use of either continuous or discrete field models of stream sediment geochemical *** map anomalies in a discrete field model of such data,two corrections are required:background correction and downstream dilution *** and geomorphology are important factors in variations of element content in stream ***,few studies have considered,through the use of digital terrain analysis,the influence of geomorphic features in downstream dilution correction of stream sediment geochemical *** study proposes and demonstrates an improvement to the traditional downstream dilution correction equation,based on the use of digital terrain analysis to map single-element anomalies in stream sediment geochemical ***,this study compares the results of analyses using discrete and continuous field models of stream sediment geochemical data from the Xincang area,*** efficiency of the proposed methodology was validated against known mineral *** results indicate that catchment-based analysis outperforms interpolation-based analysis of stream sediment geochemical data for anomaly ***,the proposed modified downstream dilution correction equation proved more effective than the original ***,further testing of this modified downstream dilution correction is needed in other areas,in order to investigate its efficiency further.

关键词： stream sediment sample catchment basin digital terrain analysis downstream dilution correction background correction

Uncertainty Visualisation of a 3D Geological Geometry Model and Its Application in GIS-Based Mineral Resource Assessment:A Case Study in Huayuan District,Northwestern Hunan Province,China

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Journal of Earth Science 2021年第2期32卷 358-369页

作者： Nan Li Cangbai Li Wenkai Chu keyan xiao Weihan Tao Xianglong Song Rui Cao Yang Xu Mingjing Fan MNR Laboratory of Metallogeny and Mineral Resource Assessment Institute of Mineral ResourcesChinese Academy of Geological SciencesBeijing 100037China Centre for Exploration Targeting the University of Western Australia35 Stirling HighwayCrawleyWA 6009Australia School of Earth Sciences and Resources China University of GeosciencesBeijing 100083China Technical Guidance Center for Mineral Resources Exploration Ministry of Natural ResourcesBeijing 100120China China Deep Exploration Center(SinoProbe Center) Chinese Academy of Geological SciencesBeijing 100037China

This paper reports an application of uncertainty visualisation of a regional scale(1:50000)3 D geological geometry model to be involved in GIS-based 3 D mineral potential assessment of the Xiangxibei lead-zinc mineral concentration area in northwestern Hunan District,***-dimensional(3 D)geological modelling is a process of interpretation that combines a set of input measurements in ***,technology has become a necessary part of GIS-based deep ***,issues of sparse data and imperfect understanding exist in the process so that there are several uncertainties in 3 D geological *** these uncertainties are inevitably transmitted into the post-processing applications,such as model-based mineral resource ***,in this paper,first,a big-data-based method was used to estimate the uncertainty of a 3 D geological model;second,a group of expectations of geological geometry uncertainty were calculated and integrated into ore-bearing stratoisohypse modelling,which is one of the major favourable parameters of assessment for Lead-Zinc(Pb-Zn)deep prospectivity mapping in northwestern Hunan;and finally,prospecting targets were improved.

关键词： uncertainty 3D geological modelling Monte Carlo simulation prospectivity mapping

Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China

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International Journal of Digital Earth 2022年第1期15卷 748-769页

作者： Zhe Chen Ruichun Chang Wenbo Zhao Sijia Li Huadong Guo keyan xiao Lin Wu Dong Hou Lu Zou College of Mathematics and Physics Chengdu University of TechnologyChengduPeople’s Republic of China Digital Hu Line Research Institute Chengdu University of TechnologyChengduPeople’s Republic of China Geomathematics Key Laboratory of Sichuan Province ChengduPeople’s Republic of China Key Laboratory of Digital Earth Science Chinese Academy of SciencesAerospace Information Research InstituteBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China International Research Centre of Big Data for Sustainable Development Goals (CBAS) BeijingPeople’s Republic of China MNR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral ResourcesChinese Academy of Geological SciencesBeijingPeople’s Republic of China

The geothermal resources in the southwest section of the Mid-Spine Belt of Beautiful China are abundant,but the quantitative prediction and evaluation of geothermal resources are very difficult. Based on geographic information system (GIS) and remote sensing (RS) platforms,six impact factors,namely land surface temperature,fault density,Gutenberg–Liszt B value,formation combination entropy,distance to river and aeromagnetic anomaly were selected. Through the establishment of the certainty factor model (CF),weights of the information entropy certainty factor model (ICF) and weights of the evidence certainty factor model (ECF),the geothermal potential in the study area were predicted quantitatively. Based on the ECF results,the six main geothermal resource areas were delineated. The results show that (1) ECF had high prediction accuracy (success index is 0.00405%,area ratio is 0.867);(2) The geothermal resource areas obtained were Ganzi–Ya’an–Liangshan,Panzhihua–Liangshan,Dali–Chuxiong,Nujiang–Baoshan,Diqing–Dali,and Lijiang–Diqing. The results provide a basis for the effective development and utilization of geothermal resources in the southwest section of the mid-ridge belt.

关键词： Mid-Spine Belt of Beautiful China certainty factor geothermal resource area weights of evidence

TO–YOLOX: a pure CNN tiny object detection model for remotesensing images

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International Journal of Digital Earth 2023年第1期16卷 3882-3904页

作者： Zhe Chen Yuan Liang Zhengbo Yu Ke Xu Qingyun Ji Xueqi Zhang Quanping Zhang Zijia Cui Ziqiong He Ruichun Chang Zhongchang Sun keyan xiao Huadong Guo School of Mathematics and Physics Chengdu University of TechnologyChengduPeople’s Republic of China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Centre of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China Geomathematics Key Laboratory of Sichuan Province Chengdu University of TechnologyChengduPeople’s Republic of China Digital Hu Line Research Institute Chengdu University of TechnologyChengduPeople’s Republic of China School of Management Science Chengdu University of TechnologyChengduPeople’s Republic of China School of Earth Science and Resources China University of Geosciences(Beijing)BeijingPeople’s Republic of China MNR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral ResourcesChinese Academy of Geological SciencesBeijingPeople’s Republic of China

Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster ***,due to interference occasioned by density,overlap,and coverage,the tiny object detection in remote sensing images has always been a difficult ***,we propose a novel TO–YOLOX(Tiny Object–You Only Look Once)***–YOLOX possesses a MiSo(Multiple-in-Singleout)feature fusion structure,which exhibits a spatial-shift structure,and the model balances positive and negative samples and enhances the information interaction pertaining to the local patch of remote sensing ***–YOLOX utilizes an adaptive IOU-T(Intersection Over Uni-Tiny)loss to enhance the localization accuracy of tiny objects,and it applies attention mechanism Group-CBAM(group-convolutional block attention module)to enhance the perception of tiny objects in remote sensing *** verify the effectiveness and efficiency of TO–YOLOX,we utilized three aerial-photography tiny object detection datasets,namely VisDrone2021,Tiny Person,and DOTA–HBB,and the following mean average precision(mAP)values were recorded,respectively:45.31%(+10.03%),28.9%(+9.36%),and 63.02%(+9.62%).With respect to recognizing tiny objects,TO–YOLOX exhibits a stronger ability compared with Faster R-CNN,RetinaNet,YOLOv5,YOLOv6,YOLOv7,and YOLOX,and the proposed model exhibits fast computation.

关键词： Tiny object detection TO-YOLOX remote sensing image deep learning attentionmechanism

Research and application of mineral resources assessment by weights of evidence model based on SIG

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Global Geology 2006年第1期9卷 109-114页

作者： Yuanyuan Chuai keyan xiao Yihua Xuan Shaobin Zhan Institute of Mineral Resources Prognosis of Synthetic Information of Jilin University Changchun 130026 Jilin China Institute of Mineral Resources Chinese Academy of Geological Sciences Beijing 100037 China College of Geo-Exploration of Science and Technology Jilin University Changrhun 130026 China

Geological data are usually of the characteristics of multi-source, large amount and multi-scale. The construction of Spatial Information Grid overcomes the shortages of personal computers when dealing with geological data. The authors introduce the definition, architecture and flow of mineral resources assessment by weights of evidence model based on Spatial Information Grid (SIG). Meanwhile, a case study on the prediction of copper mineral occurrence in the Middle-Lower Yangtze metallogenic belt is given. The results show that mineral resources assessement based on SIG is an effective new method which provides a way of sharing and integrating distributed geospatial information and improves the efficiency greatly.

关键词： SIG weights of evidence model mineral resources assessment

GIS-based uncertainty visualisation of a 3D geological geometry model and its application in mineral resource assessment: a case study in Huayuan District， northwestern Hunan Province， China

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Journal of Earth Science 2021年

作者： Nan Li Cangbai Li Wenkai Chu keyan xiao Weihan Tao Xianglong Song Rui Cao Yang Xu Mingjing Fan MNR Laboratory of Metallogeny and Mineral Resource Assessment， Institute of Mineral Resources， Chinese Academy of Geological Sciences Centre for Exploration Targeting， the University of Western Australia， 35 Stirling Highway China University of Geosciences (Beijing) Technical Guidance Center for Mineral Resources Exploration， Ministry of Natural Resources Chinese Academy of Geological Sciences

This paper reports an application of uncertainty visualisation of a regional scale （1:50，000） 3D geological geometry model to be involved in GIS-based 3D mineral potential assessment of the Xiangxibei lead-zinc mineral concentration area in northwestern Hunan District， China. Three-dimensional （3D） geological modelling is a process of interpretation that combines a set of input measurements in geometry. Today， technology has become a necessary part of GIS-based deep prospecting. However， issues of sparse data and imperfect understanding exist in the process so that there are several uncertainties in 3D geological modelling. And these uncertainties are inevitably transmitted into the post-processing applications， such as model-based mineral resource assessment. Thus， in this paper， first， a big-data-based method was used to estimate the uncertainty of a 3D geological model; second， a group of expectations of geological geometry uncertainty were calculated and integrated into ore-bearing stratoisohypse modelling， which is one of the major favourable parameters of assessment for Lead-Zinc （PbZn） deep prospectivity mapping in northwestern Hunan; and finally， prospecting targets were improved.

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中国铝土矿资源潜力预测评价

地学前缘 2018年第3期25卷 82-94页

作者：孙莉肖克炎娄德波中国地质科学院矿产资源研究所国土资源部成矿作用与资源评价重点实验室北京100037

铝土矿是铝的主要来源,我国铝土矿的产量和储量均居世界前列。为摸清我国铝土矿资源家底,制定合理的经济产业政策,需要对铝土矿资源潜力进行准确预测。我国铝土矿可分为沉积型、堆积型和红土型,以沉积型为主,占总资源储量的80%以上。沉... 详细信息

铝土矿是铝的主要来源,我国铝土矿的产量和储量均居世界前列。为摸清我国铝土矿资源家底,制定合理的经济产业政策,需要对铝土矿资源潜力进行准确预测。我国铝土矿可分为沉积型、堆积型和红土型,以沉积型为主,占总资源储量的80%以上。沉积型主要成矿时代为石炭纪和二叠纪,而堆积型和红土型为第四纪。铝土矿的成矿与碳酸盐岩和基性岩基底有关,受岩相古地理条件、古气候条件、古地貌等条件控制。在地质研究基础上,划分了18个铝土矿成矿区带,其中山西断隆区、华北陆块南缘、桂西南、桂中南、黔中等为最重要的成矿区带。在典型矿床研究的基础上,分析了三种类型铝土矿的预测要素,并建立了预测模型.采用矿床模型综合地质信息预测方法,利用计算机信息技术(特别是GIS技术),挖掘成矿信息,编制预测要素图,并采用成矿地质体体积法估算了2 000m深度以浅预测资源量。预测结果表明,我国铝土矿1 000m深度以浅有较大找矿潜力,目前资源查明率仅为35.6%,除河南、广西、山西、贵州等资源大省外,云南、重庆、山东、河北、陕西也具较大的找矿远景。预测结果为铝土矿勘查工作部署提供了科学依据。

关键词：铝土矿预测资源量潜力区地质参数体积法成矿区带