铜陵—池州地区水系沉积物测量地球化学特征及找矿方向

在长江中下游铁、铜、金多金属成矿带1∶20万水系沉积物测量的基础上,对铜陵—池州地区的地球化学异常特征进行初步总结。通过对区内元素进行因子分析,得出多种与成矿有关的异常组合,利用成矿元素异常组合划分地球化学分区,进而圈定地球化学异常。通过对比成矿地质背景和地球化学条件,对铜陵地区典型矿床进行相似度分析,预测池州地区的找矿远景区。结果表明:综合区域上的地层、构造、岩浆岩和区域矿产等资料,对成矿元素异常组合进行地球化学分区是可行的,能有效反映致矿异常,突出矿化信息;根据样点相似度值的计算结果与典型矿床相似度图的耦合情况分析,池州市南部的梅街镇、青阳县东侧、铜山铜矿外围是区域上最为有利的铜多金属找矿远景区。     

湘东南水口地区水系沉积物地球化学特征及找矿预测

利用1∶5万水口幅水系沉积物测量成果,分析研究湘东南水口地区水系沉积物地球化学特征。利用DGSInfo软件对元素分布、单元素异常、元素相关性、异常元素组合等特征进行分析研究,认为W、Sn、Bi、Cu、Ag为区内的主要成矿元素,共圈出15处地球化学综合异常。结合综合异常所处的成矿地质条件进行综合分析,划分为下湾毒砂金锑找矿远景区、联坑—渣村钨钼铜铅锌多金属找矿远景区、曾子坳—鹰咀岩—田心钨锡铜多金属找矿远景区。通过对远景区内的综合异常查证,在下湾AS1综合异常区、联坑AS3综合异常区、曾子坳AS6综合异常区均找到了与异常相关的矿化信息。结果表明,水系沉积物地球化学测量在该区具有较好的找矿效果,为研究区下一步的找矿工作指明了方向。     

宁夏贺兰山北段1∶5万化探数据的含量-面积分形异常特征及找矿预测

以宁夏贺兰山北段金铜矿产资源预测为研究对象,采用R型因子分析研究元素组合规律,应用含量-面积分形法研究地球化学异常并确定其异常下限,圈定元素组合异常和综合异常,并结合区域成矿地质背景和研究区地质-地球化学特征,划分出5处找矿远景区:哈尔木腾、柳葫芦沟-大麦里沟、树龙沟-麦子井金找矿远景区;梁根-牛头沟-其勒格金铜找矿远景区;达拉布盖钴镍找矿远景区。异常查证结果显示,梁根-牛头沟目前已控制3条工业矿体,为有利的金铜找矿靶区,其勒格已发现5处金矿化体,成矿地质条件良好,为下一步重点找矿地段。     

河南桐柏马道地区水系沉积物地球化学特征及找矿方向

通过河南桐柏马道地区1∶5万水系沉积物测量和元素地球化学特征分析研究认为,Au、Ag、Cu、Pb、Zn、Cr、Ni、Fe 8种元素为区内主要成矿元素,共圈出单元素异常831个、综合异常27个,地球化学异常严格受地层、大河断裂带及其次级断裂、岩浆岩等因素联合控制。结合综合异常特征以及成矿地质条件,圈定了围山城金银多金属矿找矿远景区、刘山岩铜锌多金属矿找矿远景区、月儿湾金银多金属矿找矿远景区、老和尚帽银多金属矿找矿远景区4处。通过对远景区综合异常查证,在围山城5-甲1-1和11-甲1-1、刘山岩14-甲3-1、月儿湾2-甲3-1、老和尚帽24-甲2-2综合异常区发现与异常相关的矿化信息,为下一步地质找矿工作指明了方向     

基于卷积自编码网络的夏河—合作地区金矿定量预测

甘肃夏河—合作地区属西秦岭成矿带, 区域地质构造复杂、矿产资源丰富。该地区已发现一定数量的金多金属矿床(点), 且在矿集区及周边地区仍存在良好的金多金属矿找矿潜力。本文以夏河—合作地区为研究区, 基于成分数据分析定量提取了成矿元素组合, 集成了以构造-地球化学异常为基础的多元信息综合找矿模型, 基于卷积自编码网络(Convolutional Auto-Encode, CAE)模型进行区域金矿产资源定量预测。结果表明CAE模型在该区的预测具有良好的性能(AUC=0.90), 以此为依据确定的7个预远景区值得进一步开展勘查工作。     

内蒙古新达来草原覆盖区钼铜多金属成矿预测

内蒙古新达来草原覆盖区位于西伯利亚板块东南大陆边缘晚古生代陆缘增生带,属古亚洲成矿域的二连—东乌旗铜钼铁晚古生代—中生代成矿带的一部分,找矿潜力巨大。该区被牧草和第四系大面积覆盖,基岩出露较差,找矿工作相对滞后。通过研究该地区区域成矿地质背景和典型矿床地质特征,分析控矿因素与找矿标志,构建综合信息预测模型,提取地质、地球化学、地球物理、遥感等找矿信息,借助GIS技术进行覆盖层“揭盖”和对找矿信息进行提取,以GeoDAS 矿产预测软件为平台,运用多重分形滤波技术区分复杂背景与异常,提取矿化弱异常,应用模糊证据权法进行信息综合集成,形成预测后验概率图,圈定找矿远景区并进行验证,探索内蒙古草原覆盖区内生金属矿床勘查与预测评价新途径。     

云南北衙地区成矿地球化学特征及找矿方向

利用北衙整装勘查区1∶20万水系沉积物地球化学数据,综合分析了地层、岩浆岩、典型矿床主要成矿元素Ag、Au、Cu、Pb、Zn的富集规律,对元素分布特征、综合异常特征、相关性及异常元素组合特征进行初步研究,确定了找矿有利指标组合;通过对比北衙金矿区典型矿床成矿地质、地球化学特征,结合地层、岩浆岩等找矿标志预测北衙地区的找矿远景区。结果表明:综合区域上的地层、构造、岩浆岩和区域矿产等资料,对成矿元素异常组合进行地球化学分区是可行的,能有效反映致矿异常,突出矿化信息;根据区域单元素Au、Ag、Pb、Zn、Cu地球化学特征以及组合元素异常特征结合成矿地质背景,指出了炭窑金矿、铅锌矿找矿靶区;马鞍山北部金矿找矿靶区,为北衙整装勘查区外围找矿提供了指导作用。     

新疆汉水泉地区水系沉积物测量地球化学特征及找矿方向

对汉水泉地区水系沉积物地球化学特征进行分析,结合工作区成矿地质条件共圈出8个综合异常。对Y1综合异常进行查证,发现3条铜矿化蚀变带,其内发育数条铜矿化体,表明水系沉积物地球化学测量在该区具有较好的找矿效果。在综合异常分析和查证的基础上,结合区域成矿地质条件、区内矿产地质特征,圈定出3处找矿远景区,即868高地铜金找矿远景区、咸水泉西部铜金找矿远景区和咸水泉石膏找矿远景区,为该区下一步找矿工作指明了方向。     

青海沟里地区水系沉积物地球化学异常特征及找矿预测

青海沟里地区是我国西北的重要金矿矿集区,为进一步探寻沟里地区的找矿潜力,以该地区1∶5万水系沉积物地球化学测量数据为研究对象,采用平均值±3倍标准差的方法提取异常下限并圈定单元素异常与综合异常,结合研究区内的地质特征对圈定的异常进行了解释与评价。在圈定找矿远景区之前,首先对研究区内德龙金矿床的水系沉积物地球化学结构特征与地质特征进行了剖析,认为北西向的断裂与Au、As组合异常是金的有效的找矿标志,并构建了地质-地球化学找矿模型,以此为指导圈定出热龙金矿找矿远景区;之后对成矿地质条件较好的、以铜为主的(HS01)综合异常进行了剖析,认为其有寻找铜多金属矿的潜力,圈定了龙洼卡鲁铜多金属矿找矿远景区。后续的异常查证工作中,在热龙找矿远景区内发现了金铅矿体,在龙洼卡鲁找矿远景区内发现了铜银矿体。本研究丰富了该区的矿产类型并为该区的找矿工作指明了新方向。     

陕西省山柞旬地区多金属矿床异常模式预测准则及地球化学环境预测

地球化学环境预测是综合信息成矿预测的重要方法之一。本文重点介绍了山（阳）柞（水）旬（阳）地区已知多金属矿床地球化学异常模式、预测准则及地球化学环境预测方法。并在预测中采用统计方法建立了本区金、铜、铅锌银及汞锑矿产地的混合优选模型及综合评价指标，同时预测了本区金和多金属矿的找矿远景区。     

宁芜盆地白象山矿区人工神经网络 三维成矿定位预测研究

本文基于三维地质环境,综合白象山矿区积累的地质资料和物探成果,首先开展三维地质建模工作,详细刻画了白象山矿区的三维地质结构;在三维地质模型基础上,利用三维空间分析手段对三维控矿因素进行定量挖掘,提取了多种三维控矿因素;最后采用人工神经网络方法进行三维成矿定位预测。预测结果显示,人工神经网络三维成矿定位预测能很好的定位出已知矿体,同时显示,在已知矿体北部及东部的深边部具有较高的成矿概率,可作为开展进一步找矿勘探的靶区。因此,人工神经网络三维成矿定位预测对于白象山矿区的应用是有效的,可服务于新老矿区的深边部三维成矿定位预测,同时可为隐伏矿、盲矿的成矿预测和优选靶区提供定量、定位新的方法和途径。     

GIS-based mineral potential modeling by advanced spatial analytical methods in the southeastern Yunnan mineral district,China

This paper demonstrates a modeling procedure of mineral potential mapping based on singularity theory, and further presents an idea to look into metallogeny of Sn–Cu polymetallic deposits in southeastern Yunnan mineral district, China by applying a localized regression method. Mineralization is a typical cascade process generally accompanied by irregular geological, geochemical and geophysical signatures. Singularity index as an efficient anomaly analytical tool helps to identify anomalies as well as characterize formation processes of these anomalies. In this study, the singularity-based mineral potential mapping method was utilized to characterize hydrothermal mineralization associated with magmatic, tectonic and sedimentary processes in this district. Based on the results, a mineral prospectivity model was constructed to delineate target areas. In addition to mineral prospectivity, controlling effects of geo-processes on mineralization are spatially non-stationary. Geographically-weighted regression analysis was thus employed to investigate these spatially-varied controlling effects and it has contributed to improve understanding to local metallogeny in the study area. Results of the spatial analysis presented can be used to guide following stages of mineral exploration in the district.     

Data fusion and porphyry copper prospectivity models,southeastern Arizona

Mineral exploration programs commonly use a combination of geological, geophysical and remotely sensed data to detect sets of optimal conditions for potential ore deposits. Prospectivity mapping techniques can integrate and analyse these digital geological data sets to produce maps that identify where optimal conditions converge. Three prospectivity mapping techniques – weights of evidence, fuzzy logic and a combination of these two methods – were applied to a 32,000 km² study area within the southeastern Arizona porphyry Cu district and then assessed based on their ability to identify new and existing areas of high mineral prospectivity. Validity testing revealed that the fuzzy logic method using membership values based on an exploration model identified known Cu deposits considerably better than those that relied solely on weights of evidence, and slightly better than those that used a combination of weights of evidence and fuzzy logic. This led to the selection of the prospectivity map created using the fuzzy logic method with membership values based on an exploration model. Three case study areas were identified that comprise many critical geological and geophysical characteristics favourable to hosting porphyry Cu mineralisation, but not associated with known mining or exploration activity. Detailed analysis of each case study has been performed to promote these areas as potential targets and to demonstrate the ability of prospectivity modelling techniques as useful tools in mineral exploration programs.     

A method for mineral prospectivity mapping integrating C4.5 decision tree,weights-of-evidence and m-branch smoothing techniques: a case study in the eastern Kunlun Mountains,China

In this study, a novel method that integrates C4.5 decision tree, weights-of-evidence and m-branch smoothing techniques was proposed for mineral prospectivity mapping. First, a weights-of-evidence model was used to rank the importance of each evidential map and determine the optimal buffer distance. Second, a classification technique that uses a C4.5 decision tree in data mining was used to construct a decision tree classifier for the grid dataset. Finally, an m-branch smoothing technique was used as a predictor, which transformed the decision tree into a probability evaluation tree. The method makes no conditional independence assumption and can be applied for class imbalanced datasets like those collected during mineral exploration for prospectivity mapping of an area. The traits of comprehensibility, accuracy and efficiency were derived from the C4.5 decision tree. In addition, a case study for iron prospectivity mapping was performed in the eastern Kunlun Mountains, China. Sixty-two Skarn iron deposits and eight evidential maps related to iron mineralization were studied. On the final map, areas of low, moderate and high potential for iron deposit occurrence covered areas of 71,491, 14,298, and 9,532 km², respectively. For the goodness-of-fit test, 91.94 % of the total 62 iron deposits were within a high-potential area, 8.06 % were within a moderate-potential area and 0 % were within a low-potential area. For ten-fold cross-validation, 82.26 % were within a high-potential area, 14.52 % were within a moderate-potential area and 3.22 % were within a low-potential area. To evaluate the predictive accuracy, Receiver Operating Characteristic (ROC) curves and Area Under ROC Curve (AUC) were employed. The accuracy of the goodness-of-fit test reached 97.07 %, and the accuracy of the ten-fold cross-validation was 95.10 %. The majority of the iron deposits were within high-potential and moderate-potential areas, which covered a small proportion of the study area.     

Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectivity analysis of Mississippi Valley‐type mineralisation

Geographic Information Systems (GIS) provide an efficient vehicle for the generation of mineral prospectivity maps, which are products of the integration of large geological, geophysical and geochemical datasets that typify modern global‐scale mineral exploration. Conventionally, two contrasting approaches have been adopted, an empirical approach where there are numerous deposits of the type being sought in the analysed mature terrain, or a conceptual approach where there are insufficient known deposits for a statistically valid analysis. There are also a variety of potential methodologies for treatment of the data and their integration into a final prospectivity map. The Lennard Shelf represents the major Mississippi Valley‐type (MVT) province in Australia; however, there are only 13 deposits or major prospects known, making an empirical approach to prospectivity mapping impractical. Instead, a conceptual approach was adopted, where critical features that control the location of MVT deposits on the Lennard Shelf, as defined by widely accepted genetic models, were translated into features related to fluid pathways, depositional traps and fluid outflow zones, which can be mapped in a GIS and categorised as either regional or restricted diagnostic, or permissive criteria. All criteria were derived either directly from geological and structural data, or indirectly from geophysical and geochemical datasets. A fuzzy‐logic approach was adopted for the prospectivity analysis, where each interpreted critical feature of the conceptual model was assigned a weighting between 0 and 1 based on its inferred relative importance and reliability. The fuzzy‐logic method is able to cope with incomplete data, a common problem in regional‐scale exploration datasets. The data were best combined using the gamma operator to produce a fuzzy‐logic map for the prospectivity of MVT deposits on the southeastern Lennard Shelf. Five categories of prospectivity were defined. Importantly, from an exploration viewpoint, the two lowest prospectivity categories occupy ～90% and the highest two categories only 1.6% of the analysed area, yet eight of the 13 known MVT deposits lie in the latter and none in the former: i.e. all lie within ～10% of the area, despite the fact that deposit locations were not used directly in the analysis. The propectivity map also defines potentially mineralised areas in the central southeastern Lennard Shelf and the southern part of the Oscar Ranges, where there are currently no known deposits. Overall, the analysis demonstrates the power of fuzzy‐logic prospectivity mapping on a semi‐regional to regional scale, and emphasises the value of geological data, particularly accurate geological maps, in exploration for hydrothermal mineral deposits that formed late in the evolution of the terrain under exploration.     

Geology,geodynamics and orogenic gold prospectivity modelling of the Paleoproterozoic Kumasi Basin,Ghana, West Africa

This paper describes the geology and tectonics of the Paleoproterozoic Kumasi Basin, Ghana, West Africa, as applied to predictive mapping of prospectivity for orogenic gold mineral systems within the basin. The main objective of the study was to identify the most prospective ground for orogenic gold deposits within the Paleoproterozoic Kumasi Basin. A knowledge-driven, two-stage fuzzy inference system (FIS) was used for prospectivity modelling. The spatial proxies that served as input to the FIS were derived based on a conceptual model of gold mineral systems in the Kumasi Basin. As a first step, key components of the mineral system were predictively modelled using a Mamdani-type FIS. The second step involved combining the individual FIS outputs using a conjunction (product) operator to produce a continuous-scale prospectivity map. Using a cumulative area fuzzy favourability (CAFF) curve approach, this map was reclassified into a ternary prospectivity map divided into high-prospectivity, moderate-prospectivity and low-prospectivity areas, respectively. The spatial distribution of the known gold deposits within the study area relative to that of the prospective and non-prospective areas served as a means for evaluating the capture efficiency of our model. Approximately 99% of the known gold deposits and occurrences fall within high- and moderate-prospectivity areas that occupy 31% of the total study area. The high- and moderate-prospectivity areas illustrated by the prospectivity map are elongate features that are spatially coincident with areas of structural complexity along and reactivation during D4 of NE–SW-striking D2 thrust faults and subsidiary structures, implying a strong structural control on gold mineralization in the Kumasi Basin. In conclusion, our FIS approach to mapping gold prospectivity, which was based entirely on the conceptual reasoning of expert geologists and ignored the spatial distribution of known gold deposits for prospectivity estimation, effectively captured the main mineralized trends. As such, this study also demonstrates the effectiveness of FIS in capturing the linguistic reasoning of expert knowledge by exploration geologists. In spite of using a large number of variables, the curse of dimensionality was precluded because no training data are required for parameter estimation.     

Effects of misclassification costs on mapping mineral prospectivity

Mineral prospectivity mapping is a classification process because in a given study area, a specific region is classified as either a prospective or non-prospective area. The cost of false negative errors differs from the cost of false positive errors because false positive errors lead to wasting much more financial and material resources, whereas false negative errors result in the loss of mineral deposits. Traditional machine learning algorithms using for mapping mineral prospectivity are aimed to minimize classification errors and ignore the cost-sensitive effects. In this study, the effects of misclassification costs on mapping mineral prospectivity are explored. The cost-sensitive neural network (CSNN) for minimizing misclassification costs is applied to map Fe polymetallic prospectivity in China’s southwestern Fujian metalorganic belt (SFMB). A CSNN with a different cost ratio ranging from 1:10 to 10:1 was used to represent various misclassification costs. The cross-validation results indicated a lower misclassification cost compared to traditional neural networks through a threshold-moving based CSNN. The CSNN’s predictive results were compared to those of a traditional neural network, and the results demonstrate that the CSNN method is useful for mapping mineral prospectivity. The targets can be used to further explore undiscovered deposits in the study area.     

西藏革吉县尕尔穷铜金矿区岩石地球化学勘查异常特征及找矿方向

尕尔穷矿区具有三种类型矿化:产于斑岩型中的铜钼(金)矿化、产于矽卡岩中的铜金矿化和产于构造破碎带中的铜金矿化,三种矿化类型同属斑岩成矿系统。岩石地球化学测量结果表明,矿区存在R-1、R-2和R-3等三个主要异常,三个异常区的元素分布特征大体相似,R-1由异常浓集中心向外具有Cu-Mo-Se-Te-(Bi)-Pb-Zn-As-Sb-Tl的元素分带现象,这种元素组合及分带具有高温→中低温成矿演化的特点,特征与斑岩成矿系统很相似,具有寻找斑岩型矿床的潜力;R-2异常区Te、Se、Sb、Pb、Zn等外围元素在此范围内较弱的异常和Cu、Mo的强异常表明此异常区遭受剥蚀程度较大;R-3异常区是与石英闪长岩和花岗闪长岩有关的斑岩型矿化、矽卡岩矿化和构造破碎带中的矿化,其中以矽卡岩型和构造破碎带中的矿化的找矿前景最好,深部斑岩型矿化也具有一定的找矿潜力。     

Deriving Optimal Exploration Target Zones on Mineral Prospectivity Maps

This paper describes a quantitative methodology for deriving optimal exploration target zones based on a probabilistic mineral prospectivity map. The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought was considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then put into an objective function in simulated annealing in order to derive a set of optimal exploration focal points. Each optimal exploration focal point represents a pixel or location within a circular neighborhood of pixels with high posterior probability of mineral deposit occurrence. Buffering of each optimal exploration focal point, based on proximity analysis, resulted in optimal exploration target zones. Many of these target zones coincided spatially with at least one occurrence of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study showed the usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map.     

鄂东南矿集区典型矽卡岩-斑岩矿床蚀变矿物短波红外(SWIR)光谱研究与勘查应用

鄂东南矿集区是我国著名的矽卡岩-斑岩型铜铁金矿床产区,研究程度较高,近年来矿产勘查进入了深部找矿的新阶段,需要较新的勘查方法与思路。本文介绍了利用短红外光谱(SWIR)技术,对鄂东南典型的铜绿山矽卡岩型Fe-Cu-Au矿床、鸡冠嘴矽卡岩型Cu-Au矿床、铜山口矽卡岩-斑岩型Cu-Mo-W矿床进行的蚀变矿物综合研究最新成果。在确定蚀变矿物组合类型、形成期次、蚀变分带的基础上,揭示了主要蚀变矿物SWIR特征值的指示规律,并进一步提取各个矿床的蚀变矿物SWIR勘查标志,如铜绿山矿床富Fe绿泥石(Pos2250 2253nm)、高结晶度高岭石族(Pos2170 2170nm,Dep2170 0. 18)、白云母族-蒙脱石异常Pos2200值( 2212nm或2202nm)、高岭石、迪开石及皂石的大量出现,可以作为铜绿山铜铁金矿床有效的蚀变矿物勘查标志;鸡冠嘴矿床白云母族-蒙脱石Pos2200特征值的高值( 2209nm)区域对矿体位置具有较好的指示性;铜山口矿床绿泥石的高Fe-OH吸收峰位值(Pos2250 2249nm)和高Mg-OH吸收峰位值(Pos2335 2333nm)的高频出现,可以作为铜山口铜钼钨矿床的有效勘查标志。这些研究成果表明蚀变矿物可以为鄂东南矿集区提供有效的勘查标志体系,同时,这些最新的勘查标志也在铜绿山铜铁金矿床得到了初步的应用和验证。