Porphyry Copper Mineral Prospectivity Mapping Using Interval Valued Fuzzy Sets Topsis Method in Central Iran

Geospatial Information System (GIS) provide tools to quantitatively analysis and combination of datasets from geological, geophysical, remote sensing and geochemical surveys for decision-making processes. Excellent coverage of well-documented and good quality data enables testing of variable exploration modeling in an efficient way. The study area of this research is the most important part of Cu (Mo) porphyry—type mineralization belt in Iran. There are some well-known porphyry copper deposits in this region like Sarcheshmeh and Meiduk mines, but certainly there are same grounds to search for new porphyry deposits. The risks of developing mineral resources need to be known as accurately as possible, with regarding to all features those are effective in mineralization. These features can be recognized respect to Critical Genetic Factors (CGF’s) using Critical Recognition Criteria (CRC) for each type of mineralization. CGF’s can be employed for designing a Conceptual Genetic Model (CGM). Evidence maps create on the basis of CGM and then integrate together for production of Mineral Prospectivity Map (MPM). This map categorizes the areas based on their exploration importance. There are several techniques for creation of MPM. Interval Valued Fuzzy Sets (IVFSs) TOPSIS method was applied in this research. This method as a knowledge-driven method, allocate appropriate weights to layers on the basis of the effective membership, non membership, and non-certainty. The fundamental concept of TOPSIS is that the chosen alternatives should have the shortest distance from the positive ideal points (A*) and the farthest distance from negative ideal points (A-).

Mineral Prospectivity Mapping Method Integrating Multi-Sources Geology Spatial Data Sets and Case-Based Reasoning

Extracting and synthesizing information from existing and massive amounts of geology spatial data sets is of great scientific significance and has considerable value in its applications. To make mineral exploration less expensive, more efficient, and more accurate, it is important to move beyond traditional concepts and establish a rapid, efficient, and intelligent method of predicting the existence and location of minerals. This paper describes a case-based reasoning (CBR) method for mineral prospectivity mapping that takes spatial features of geology data into account and offers an intelligent approach. This method include a metallogenic case representation that combines spatial and attribute features, metallogenic case-based storage organization, and a metallogenic case similarity retrieval model. The experiments were performed in the eastern Kunlun Mountains, China using CBR and weights-of-evidence (WOE), respectively. The results show that the prediction accuracy of the CBR is higher than that of the WOE.

Lead and Copper Mineral Prospectivity Mapping in Kalatereshm Area, Based on Exploratory Data Sets Using AHP-Index Overlay Modeling in GIS (Semnan Province, North of Iran)

The Kalatereshm is an area in north of Iran which covers some part of Torud magmatic belt. The area of this belt is about 2000 square kilometers and most of the mines in this particular area are of Copper, lead and Zinc. The Synthesis process is done by the Analysis Hierarchy Process (AHP) and Index Overlay (IO) methods. Of previous studies on the area, various companies providing Geological maps and in particular the company of Jiangxi providing its own geochemical maps can be mentioned. The reasons for doing this research and its innovation in Kalatereshm’s sheet can be justified as to be valuable and the fact that we would be able to save in time and cost by doing so. Previous case studies on this particular region lacked the necessary use of an advanced software and method. The informational layers included geochemical layers (the second and first ratings were given to Copper and Lead respectively by weighting based on AHP method), geology layer (the fourth and second ratings were given to Copper and Lead respectively by weighing based on AHP method), fault layer (the first and fourth ratings were given to Copper and Lead respectively by weighting based on AHP method), satellite imagery layer (the third rating was given to both Copper and Lead by weighting based on AHP method) and the more applicable areas for field exploration and detailed procedures of exploration had been determined (the mentioned ratings were delineated by each element’s respective weight in each layer and their importance in the Synthesis of informational layers).

Geospatial Mapping of Singhbhum Shear Zone (SSZ) with Respect to Mineral Prospecting

Singhbhum Shear Zone is a highly mineralized zone having variety of minerals, predominantly those of uranium, copper and some sulphide minerals. From Remote Sensing data it is possible to decipher the regional lithology, tectonic fabric and also the geomorphic details of a terrain which aid precisely in targeting of metals and minerals. Mapping of mineralized zones can be done using Geospatial Technology in a GIS platform. The present study includes creation of various maps like lithological map, geomorphological map, contours and slope map using satellite data like IRS LISSIV and ASTER DEM which can be used to interprete and correlate the various mineral prospective zones in the study area. Even the alterations of the prevalent mineral zones can be mapped for further utilization strategies. The present work is based on the investigations being carried under ISROSAC Respond Project (Dept. of Space, Govt. of India SAC Code: OGP62, ISRO Code: 10/4/556).

Explainable artificial intelligence models for mineral prospectivity mapping

Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectively handle and interpret.Artificial intelligence(AI)algorithms,which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration,have demonstrated excellent performance in MPM.However,AI-driven MPM faces several challenges,including difficult interpretability,poor generalizability,and physical inconsistencies.In this study,based on previous studies,we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence(XAI)models for MPM by embedding domain knowledge throughout the AI-driven MPM,from input data to model design and model output.This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process,thereby improving model interpretability and performance.Overall,the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process,presenting a valuable and promising area for future research designed to improve MPM.

Discipline progress of mineral mapping of gold deposit in China

Sanjiang and Jiaodong areas are the important gold regions in China.The gold mineralization is correlated with the Mesozoic faults and its derivative faults in the granite or the contact zone between granite and base rock.The mineral mapping is one of the important approaches of prospecting mineralogy.The temporal and spatial band distribution of mineral,mineral combination and mineral typomorphism features are natural regularity rules,but this kind of band feature is sometimes hidden and thus in need of mineral typomorphism research and mapping to disclose this band feature and to serve for the prospecting of mineral.The changes of the mineral typomorphism feature are often embodied in the"through"mineral,the typomorphism features of the through mineral change like the gradient toward the ore body,and the changes of the features are most obvious and strongest toward the orebodies.The new American mineralogy mapping indicates the weak change of the mineral composition or structure will lead to the change of the shape and wave length position of the specific absorbent feature.Australian experiment lab investigation has held the reason of a series of mineral.The progress achievement of the remote sensing and computer analysis technology now permits a direct comparison directly between the lab and remote sensing data,so the good feature database of the experiment light spectroscopy can serve for drawing of the spacial distribution and remote sensing material.Mineralogy mapping raised the extensive emphasis in the international earth arena.

A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China

Recent studies have pointed out that the widespread iron deposits in southwestern Fujian metallogenic belt(SFMB)(China) are skarn-type deposits associated with the Yanshanian granites. There is still excellent potential for mineral exploration because large areas in this belt are covered by forest. A new predictive model for mapping skarn-type Fe deposit prospectivity in this belt was developed and focused on in this study, using five criteria as evidence:(1) the contact zones of Yanshanian granites(GRANITE);(2) the contact zones within the late Paleozoic marine sedimentary rocks and the carbonate formations(FORMATION);(3) the NE-NNE-trending faults(FAULT);(4) the zones of skarn alterations(SKARN); and(5) the aeromagnetic anomaly(AEROMAGNETIC). The fuzzy weights of evidence(FWof E) method, developed from the classical weights of evidence(Wof E) and based on fuzzy sets and fuzzy probabilities, could provide smaller variances and more accurate posterior probabilities and could effectively minimize the uncertainty caused by omitted or wrongly assigned data and be more flexible than the Wof E. It is an efficient and widely used method for mineral potential mapping. Random forests(RF) is a new and useful method for data-driven predictive mapping of mineral prospectivity method, and needs further scrutiny. Both prospectivity results respectively using the FWof E and RF methods reveal that the prediction model for the skarn-type Fe deposits in the SFMB is successful and efficient. Both methods suggested that the GRANITE and FORMATION are the most valuable evidence maps, followed by SKARN, AEROMAGNETIC, and FAULT. This is coincident with the skarn-type Fe deposit mineral model in the SFMB. The unstable performance experienced when FORMATION was omitted might indicate that the highest uncertainty and risk in follow-up exploration is related to the sequences. In addition, the performance of the RF method for the skarn-type Fe deposits prospectivity in the SFMB is better than the FWof E; therefore, it could be used to guide further exploration of skarn-type Fe prospects in the SFMB.

金属矿山深部资源三维智能预测理论与方法

当前,中国主要矿产大多面临后备资源不足问题,在大中型矿山深部寻找可接替资源已成为保障主要矿产的战略措施。然而,金属矿山深部找矿预测面临着矿床深部结构不清、深部控矿规律隐蔽、深部矿体空间定位难度大等关键问题,亟须建立适应矿山真三维空间要求的矿产资源预测与评价新理论并取得关键技术突破。针对这些问题,自20世纪80年代开始,笔者通过持续的探索和研究创新,先后提出隐伏矿体立体定量预测、深部资源三维可视化预测,最终提出并建立深部资源三维智能预测理论与方法,实现了矿床深部三维结构重建的自动化与精细化、深部控矿规律表征的定量化与透明化、深部成矿空间矿体定位的精准化与智能化。该理论与方法主要包括成矿系统分析与找矿概念模型构建、矿床深部结构贝叶斯数据同化三维精细重建、矿床深部地质结构三维几何-物质分析与成矿信息提取、深部矿体空间定位规律深度学习与三维预测等重要方法与关键技术。该理论与方法先后在山东胶西北金矿集区、甘肃金川铜镍矿等地取得成功应用,在矿区深部探获厚度大、高品位矿体,取得深部找矿突破。

找矿知识图谱的智能化应用:以钦杭成矿带斑岩铜矿为例

找矿知识图谱是一个结构化的找矿知识表示形式,它通过将各种矿床地质、矿物、矿体和勘探开发技术等要素以及要素之间的关系进行关联和表达,形成一个知识图谱结构,为矿产资源的预测和评估提供了新的途径。本研究从数据源和文献中收集了钦杭成矿带斑岩铜矿的相关知识,利用自然语言处理技术构建了知识图谱并和机器学习技术进行知识的自动化提取和推理。本研究通过构建找矿知识图谱模型,对钦杭成矿带斑岩铜矿的实体、属性和关系进行了表达和存储。在此基础上,利用自然语言处理技术对知识图谱进行了语义关联和推理,实现了知识的自动化提取和推理。本研究还建立了查询问答和可视化系统,使用户可以通过查询实体、属性或关系来获取相关信息,并以可视化的方式展示钦杭成矿带斑岩铜矿信息的结构和关联。最后,本研究通过实验和测试验证了基于知识图谱的智能应用在钦杭成矿带斑岩铜矿找矿工作中的有效性和准确性。与传统方法相比,该应用能够在短时间内提供更全面、准确的找矿建议。未来,将深化图算法应用和推荐系统,以满足不同场景下的找矿需求,并拓展其在其他相关领域的应用潜力。

河南围山城金银矿集区三维地质建模与成矿预测

三维建模与成矿预测已成为当前国内外十分流行且较成熟的技术方法。通过研究银洞坡金矿和破山银矿成矿地质特征,对围山城矿集区开展三维可视化模型构建及深部成矿预测。利用82条地质剖面建立了地质体(地层、岩体、矿体)、构造模型,利用1∶5万重磁数据和63条电测深数据建立了密度、磁化率、电阻率等地球物理模型,利用52条岩石剖面、614个钻孔、117个探槽、56个浅井及94个平硐数据建立了地球化学模型,实现了地、物、化数据的综合集成。以“三位一体”找矿预测理论为指导,建立了研究区岩浆热液型Au、Ag矿床三维定量预测模型,确定了Au、Ag矿床成矿有利信息变量;采用证据权法提取了预测变量的权重值和相关程度,选择合适的变量进行成矿预测,利用C-V分形确定阈值,圈定了6个找矿靶区,选择新庄预测靶区(A 1)进行钻孔验证,发现金矿体6条,充分证明了利用证据权法进行三维成矿预测具有可行性与科学性,圈定的靶区对矿山进行深边部隐伏矿体找矿具有重要的实践意义。

蚀变矿物填图技术在地质找矿勘查中的应用

在某金属矿区的勘探过程中,地质勘查团队利用蚀变矿物填图技术,利用它的技术优势,包括算法改进、多源数据融合的应用,以及人工智能与机器学习在蚀变矿物填图中的潜力。深入探讨蚀变矿物填图技术在地质找矿勘查中,对异常信息处理,圈定了几个高潜力的金矿化靶区。期望为提升找矿勘查的精准度和效率提供有力的技术支持。

Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data

Today’s era of big data is witnessing a gradual increase in the amount of data,more correlations between data,as well as growth in their spatial dimension.Conventional linear statistical models applied to mineral prospectivity mapping(MPM)perform poorly because of the random and nonlinear nature of metallogenic processes.To overcome this performance degradation,deep learning models have been introduced in 3 D MPM.In this study,taking the Huayuan sedimentary Mn deposit in Hunan Province as an example,we construct a 3 D digital model of this deposit based on the prospectivity model of the study area.In this approach,3 D predictor layers are converted from the conceptual model and employed in a 3 D convolutional neural network(3 D CNN).The characteristics of the spatial distribution are extracted by the 3 D CNN.Subsequently,we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3 D CNN model and weight of evidence(WofE)method on each group.The predictive model is trained on the basis of the coupling correlation between the spatial distributions of the variables and the underground occurrence space of the Mn orebodies,and the correlation between different ore-controlling factors.The analysis of 12 factors indicates that the 3 D CNN model performs well in the 3 D MPM,achieving a promising accuracy of up to 100%and a loss value below 0.001.A comparison shows that the 3 D CNN model outperforms the WofE model in terms of predictive evaluation indexes,namely the success rate and ore-controlling rate.In particular,the 1–12 ore-controlling factors selected in experiment 5 provide a significantly better prediction effect than the other factors.Consequently,we conclude that the Mn deposit in the study area is not only related to the stratum and interlaminar anomalous bodies but also to the spatial distribution of the faults.The experimental results confirm that the proposed 3 D CNN is promising for 3 D MPM as it eliminates the interference factors.

云南省东南部新寨锡矿床矿化趋势面分析

系统收集整理了云南省东南部新寨锡矿资源勘查过程中形成的85个探矿工程编录资料,建立了三维空间数据库,对具有空间属性的锡品位数据进行了基本统计分析和矿化趋势分析,并绘制了矿化等值线图和矿化趋势图。认为新寨矿区矿化富集带走向与矿化趋势等值线走向均与地层走向方向一致,显示出赋矿地层对锡的富集具有明显的控制作用;矿区南东浅部矿化富集带较北西深部矿化富集带锡矿化强度高,总体矿化趋势北西低南东高,结合地质背景推测矿区发育的向斜挠曲构造影响了锡的富集,远离向斜轴部锡品位有增强趋势。

蚀变矿物填图技术在地质找矿勘查中的应用探究

随着现代化技术以及信息化手段的飞速发展,社会已经进入到了全新的发展进程中,这也为矿业领域的发展起到了良好的促进作用,而其中所采用的各类传统找矿勘查技术不仅在效率上比较低,整体勘查效果也相对较差。为了有效提高整体地质找矿勘查质量,就应当在内部合理引入蚀变矿物填图技术,以此为基础来更好地处理地质找矿过程中出现的异常信息,提升找矿勘查的安全性与稳定性。因此,文章首先对蚀变矿物填图技术的基本概述加以明确;其次,对蚀变矿物填图技术矿物信息的主要提取方式展开深入分析;在此基础上,提出蚀变矿物填图技术在地质找矿勘查中的具体应用措施。

便携式短波红外矿物分析仪(PIMA)在西藏墨竹工卡县驱龙铜矿区矿物填图中的应用

介绍了应用国内近两年新引进的便携式短波红外矿物分析仪(PortableInfraredMineralAnalyser,简称PIMA)在西藏驱龙铜矿区所做的矿物填图的工作情况,以及依据PIMA测试所获得的光谱数据进行矿物识别的初步研究成果。通过野外对探槽TC0701和TC1101系统采样和对应的光谱测试分析,识别出了与矿化和矿体有直接关系的蚀变指示矿物硬石膏(anhydrite)。在矿物识别过程中,提取到了可以作为矿区标准光谱的白云母等矿物的纯光谱曲线,并且在野外验证过程中确认了航天图像(Hyperion)上解译的零星分布的蚀变矿物绿泥石的存在。

金矿床的矿物蚀变与矿物标型及其找矿意义

绝大多数金矿床的形成或多或少都与热液活动有关。成矿过程中伴随流体的运移和演化,流体与围岩会不断发生水-岩反应并导致金物质在适合的构造空间堆积成矿,同时在流体途经区留下蚀变产物。这一过程中形成的蚀变矿物特征和矿物组合能够指示成矿过程的物理化学条件的变化,是找矿的重要信息。这一能够标识成矿过程的矿物学特征统称为矿物标型特征。基于与金矿矿化关系密切的常见矿物蚀变现象及其时空分布规律,并以矿物标型特征参数（如载金矿物黄铁矿热电性特征、蚀变矿物短波红外光谱特征和蚀变岩磁化率特征等）为基础,通过蚀变矿物分带和矿物学填图,可以建立成矿模式和找矿模型,进而指导找矿勘查。本文通过对多个矿床实例的总结分析,认为以矿物标型特征为主要参数的矿物学填图是有效缩小找矿靶区或进行深部预测的找矿方法之一。

基于航空成像高光谱数据的玉石矿找矿要素识别和找矿预测——以青海省纳赤台地区为例

航空成像高光谱遥感具有“图谱合一”的特点,同时具备高空间分辨率和高光谱分辨率,能够根据地物光谱特征直接识别矿物,在地质调查中取得了较好的应用效果（王润生等,2011;Freek et al.,2012;Laakso et al.,2015;Zhao et al.,2015）。

大洋洲地区系列地质图件编制

通过资料收集、资料取舍、建立数据库、图件编制与校核,编制完成了大洋洲地区1∶500万比例尺的地质图、大地构造图、矿产图、成矿区划和找矿预测图等主要图件,多幅单矿种矿产图等次要图件,物探、化探、遥感信息等辅助图件,以及局部地区(州)或重要矿种的参考图件,是一次对大洋洲地区地质矿产资料的大集成。通过编图和建库初步熟悉了大洋洲地区的地质矿产概况,为查询、访问和提取大洋洲地区的地质矿产资料提供了方便。

典型矿集区成矿地质事件研究和填图方法初探

针对中等尺度范围(或中等比例尺)区域成矿潜力和已知矿山的深部及外围资源评价等问题,试图用成矿地质事件方法开展相关研究。成矿地质事件评价方法,就是在恢复矿床形成过程并建立成矿模式的基础上,通过对矿源岩系的构造变形岩相形迹追索来恢复与成矿有重要意义的地质事件及其演化过程,研究在这一系列地质事件中成矿物质的分布规律和逐步富集成矿的标志及程度,并用以进一步探索、指导区域资源潜力评价和填图找矿的方法。应用这一思路对南岭中段锡多金属矿和胶东矿集区金矿进行了区域成矿地质事件研究和地质填(编)图方法的初步探讨。

数字填图技术在战略性矿产远景调查填图中的应用

1∶5万瑞金市幅战略性矿产远景调查填图采用了数字填图技术。本文详细介绍了数字填图的前期准备工作、野外PRB过程、PRB数据室内资料整理操作过程、成果表达等内容。我们的实践表明,数字填图技术实现了矿产地质调查填图野外观察图、文、像的数字化,改变和优化了传统的矿产地质填图的精度和资料整合方式,大幅度提高了矿产调查的工作效率,缩短了找矿填图周期。