Exploration feature selection applied to hybrid data integration modeling: Targeting copper-gold potential in central Iran

A Sugeno-type fuzzy inference system is implemented in the framework of an adaptive neural network to map Cu–Au prospectivity of the Urumieh–Dokhtar magmatic arc (UDMA) in central Iran. We use the hybrid “Adaptive Neuro Fuzzy Inference System” (ANFIS; Jang, 1993) algorithm to optimize the fuzzy membership values of input predictor maps and the parameters of the output consequent functions using the spatial distribution of known mineral deposits. Generic genetic models of porphyry copper–gold and iron oxide copper–gold (IOCG) deposits are used in conjunction with deposit models of the Dalli porphyry copper–gold deposit, Aftabru IOCG prospect and other less important Cu–Au deposits within the study area to identify recognition criteria for exploration targeting of Cu–Au deposits. The recognition criteria are represented in the form of GIS predictor layers (spatial proxies) by processing available exploration data sets, which include geology, stream sediment geochemistry, airborne magnetics and multi-spectral remote sensing data. An ANFIS is trained using 30% of the 61 known Cu–Au deposits, prospects and occurrences in the area. In a parallel analysis, an exclusively expert-knowledge-driven fuzzy model was implemented using the same input predictor maps. Although the neuro-fuzzy analysis maps the high potential areas slightly better than the fuzzy model, the well-known mineralized areas and several unknown potential areas are mapped by both models. In the fuzzy analysis, the moderate and high favorable areas cover about 16% of the study area, which predict 77% of the known copper–gold occurrences. By comparison, in the neuro-fuzzy approach the moderate and high favorable areas cover about 17% of the study area, which predict 82% of the copper–gold occurrences.     

Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry

A genetic algorithm (GA)-based neuro-fuzzy approach is used for identification of geochemical anomalies by implementing a Takagi, Sugeno and Kang (TSK) type fuzzy inference system in a 5-layered feed-forward adaptive artificial neural network. This paper investigates the effectiveness of GA-based neuro-fuzzy for separating zone dispersed mineralization (ZDM) from blind mineralization, and its application for identification of geochemical anomalies in the arid landscape of the Lut metallogenic province in eastern Iran. Other classification algorithms such as metallometry, zonality, criteria, and back-propagation artificial neural network classifiers are also used for comparison. The genetic operators are carefully designed to optimize the artificial neural network, avoiding premature convergence and permutation problems. The results show that the GA-based hybrid neuro-fuzzy model can provide accurate results in comparison with those results obtained by other techniques. Neuro-fuzzy and GA-based neuro-fuzzy techniques appear to be well-suited for routine exploration geochemistry applications. In conjunction with statistics and conventional mathematical methods, hybrid approaches can be developed and may prove a step forward in the practice of applied geochemistry.     

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.     

山阳-柞水矿集区斑岩-矽卡岩型多金属矿床找矿方法组合研究

山阳-柞水矿集区构造-岩浆活动强烈,成矿作用显著,以发育新元古代、晚三叠世、晚侏罗世-早白垩世三期岩浆作用及其相关的斑岩-矽卡岩型铜钼-金-铁多金属矿床为特征。这些不同类型矿床的找矿方法在不同找矿阶段完全不同。其中预查选区阶段在综合研究基础上,采用以1∶50000水系沉积物测量为主,结合1∶100000航磁解译或1∶50000高精度磁测,地质地球化学剖面与激电/磁法剖面相互验证,以快速圈定靶区或矿化带;普查评价阶段在1∶10000~1∶2000地质填图、大比例尺遥感解译及蚀变信息提取基础上,通过1∶10000或1∶5000土壤正规网测量、高精度磁测或激电扫面、可控源音频大地电磁测深、高光谱蚀变矿物填图、地物化综合剖面等方法综合确定含矿地段,依据工程验证控制进一步有效追索、圈连矿(化)体。通过这些找矿方法组合,目前已在山阳-柞水矿集区内发现池沟铜钼矿床、冷水沟铜矿床和白沙沟金矿点等。     

Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data

Mineral targets are local geological anomalies. In a study area of a number of unit cells, mapping mineral prospectivity can be implemented by identifying anomaly cells from the unit cell population. One-class support vector machine (OCSVM) models can yield useful results in anomaly detection in high-dimensional data or without any assumptions on the distribution of the inlying data. The OCSVM model was applied to mapping gold prospectivity of the Laotudingzi-Xiaosiping district, an area with a complex geological background, in Jilin Province, China. The decision function value of each unit cell belonging to an anomaly was computed on the basis of the trained OCSVM model and used to express gold prospectivity of the cell. The receiver operating characteristic (ROC) curve, area under curve (AUC) and data-processing efficiency were used to compare the performance of the OCSVM model and a restricted Boltzmann machine (RBM) model in mapping gold prospectivity. The results show that the OCSVM model outperforms the RBM model in terms of ROC, AUC and data-processing efficiency. Gold targets were optimally delineated by using the Youden index to maximise the spatial association between the delineated gold targets and known gold deposits. The gold targets delineated by the OCSVM model occupy 11% of the study area and contain 88% of the known gold deposits; and the gold targets delineated by the RBM model occupy 10% of the study area and contain 81% of the known gold deposits. Therefore, the OCSVM model is a feasible mineral prospectivity mapping approach.     

西昆仑落石沟一带铅锌矿成矿特征及区域预测

西昆仑是新疆西南部重要的成矿区带,落石沟矿带已发现铁多金属、铜、铅锌矿（床）点16处,成矿潜力巨大。从区域地质成矿背景、控矿构造、沉积建造分析入手,以宝塔山、落石沟铅锌矿作为典型矿床,结合重力、磁法、化探、遥感等信息建立了区域预测概念模型,初步总结了该区铅锌矿的控矿规律,确定了区域宏观与微观预测要素。在此基础上运用证据权法采用地质、地球化学、地球物理、遥感等12个变量进行了成矿后验概率计算,后验概率最大值为0.854 022,最小值为0.000 015。按3级分类,成矿最为有利地区后验概率为0.643 428,较为有利地区后验概率为0.432 833,一般有利地区后验概率为0.222 238。按一定颜色进行分区,根据后验概率图圈定了5个最小预测区。对预测区进行了地质评述,结果表明,西昆仑落石沟一带是寻找层控-热液型铅锌矿较为有利的地区。     

Mineralized and unmineralized calderas in Spain; Part I, evolution of the Los Frailes Caldera

The Cabo de Gata volcanic field of southeastern Spain contains several recently-recognized calderas. Some of the calderas are mineralized with epithermal gold, alunite, and base metal deposits, and others are barren, and yet they formed under generally similar conditions. Comparison of the magmatic, geochemical, and physical evolution of the Los Frailes, Rodalquilar, and Lomilla calderas provides insight into the processes of caldera evolution that led to precious-metal mineralization. The Los Frailes caldera formed at 14.4 Ma and is the oldest caldera. It formed in response to multiple eruptions of hornblende dacite magma. Following each eruption, the area collapsed and the caldera was invaded by the sea. Dacite domes fill the lower part of the caldera. Pyroxene andesites were erupted through the solidified core of the caldera and were probably initially responsible for magma generation. The Los Frailes caldera did not evolve to rhyolites nor was it subjected to the amount of structural development that the younger, mineralized Rodalquilar and Lomilla calderas were.     

Prediction of the Rock Mass Diggability Index by Using Fuzzy Clustering-Based,ANN and Multiple Regression Methods

Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.     

赣南于都金银多金属矿整装勘查区1:50 000银坑幅矿产地质图数据集

本数据集综合应用岩性-构造-矿化蚀变专项填图、遥感、物探、化探、钻探等多种方法进行数据采集,完成路线调查长度520.65 km,采集水系沉积物样品2 047件,遥感数据解译面积461 km²,化学分析样167件。重点对燕山早期成矿岩浆岩、含矿建造、控岩控矿构造,以及矿化蚀变标志等进行了调查划分,明确区内银金多金属矿主要成矿地质体为燕山早期花岗闪长岩,控岩控矿构造为北东向逆冲断裂及其次级裂隙。在此基础上,编制了1：50 000银坑幅建造构造图,利用数字化填图系统（DGSS）创建了区内矿产地质图数据库,全面汇编了重要建造构造、地质界线、断裂、岩浆岩等图层属性库;完善了银坑式中低温岩浆期后热液矿床找矿预测模型,突出了区内矿产与建造构造之间的成因联系。     

黔南台陷碳酸盐型铅锌矿床成矿条件及找矿模型

志留纪末期广西运动，使扬子淮地台与华南褶皱带碰拼接在一起形成统一地台后，在泥盆纪时期地台活化为张性，晚泥盆世产生了沿基地断裂的热液活动并成矿，这就是黔南台陷成矿的地质背景，通过典型矿床特征分析，把黔南台陷中的铅锌矿床分为两类；即与同生沉作用相关的锌矿床和以顺层充填和交代作用为主的热液型铅锌矿床，对成矿地质条件进行分析，指出了区域成矿模式及找矿模型，认为黔南台陷铅锌矿床成因为产于碳酸盐岩中的沉积改造型铅锌矿床和热液型铅锌矿床。     

基于自适应神经-模糊推理系统的软土路基沉降预测模型

针对目前软基沉降预测中最常用的生长曲线模型以及人工神经网络模型的不足,提出将自适应神经模糊推理系统(ANFIS)应用于软基沉降预测。ANFIS将专家的模糊推理过程蕴含于神经网络结构中,使神经网络的结点和权值具有明确的物理意义,避免了传统神经网络工作过程的"黑盒"性。同时该系统可以采用最小二乘法和梯度下降法相结合的混合算法,既具有神经网络的自适应性和学习能力,又克服了它的局部极小值等缺点,预测精度也远高于生长曲线模型。最后用工程实例与生长模型和神经网络模型进行了对比,结果表明:ANFIS模型优于这两种模型,特别是在模拟多输入变量、高维数下软基沉降预测问题时有着独特的优势,具有一定的推广应用价值。     

Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit

This paper describes the usage of clustering methods including self-organizing map (SOM) and fuzzy c-means (FCM) which are applied to prepare mineral prospectivity map. Different evidential layers, including geological, geophysical, and geochemical, to evaluate Now Chun copper deposit located in the Kerman province of Iran are used. Clustering approaches are used to reduce the dimension of 13 feature vectors derived from different layers. At first, Geospatial Information Systems (GIS) is employed to analyze and integrate different layers, and the area under study is prioritized to five classes. Then, the SOM as an unsupervised classification method is carried out to classify this area into five clusters. Produced clusters are compared with GIS prospect map, while the SOM results are matched with the GIS output. The main reason to use the FCM is that a vector belongs simultaneously to more than one cluster so that membership values of each cluster can be mapped. As a consequence, clusters generated by the SOM and FCM are considerably matched with five-class-map of the GIS approach. The chosen cluster as a high potential location to additional drilling is matched to the main alteration and faults zone. To validate generated clusters for mineral potential mapping, geological matching of study area and selected proper cluster can be a satisfactory way. Finally, clustering methods can be a very fast approach to interpret the area under study.     

A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model

Ground vibration is one of the common environmental effects of blasting operation in mining industry, and it may cause damage to the nearby structures and the surrounding residents. So, precise estimation of blast-produced ground vibration is necessary to identify blast-safety area and also to minimize environmental effects. In this research, a hybrid of adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) was proposed to predict blast-produced ground vibration in Pengerang granite quarry, Malaysia. For this goal, 81 blasting were investigated, and the values of peak particle velocity, distance from the blast-face and maximum charge per delay were precisely measured. To demonstrate the performance of the hybrid PSO–ANFIS, ANFIS, and United States Bureau of Mines empirical models were also developed. Comparison of the predictive models was demonstrated that the PSO–ANFIS model [with root-mean-square error (RMSE) 0.48 and coefficient of determination (R ²) of 0.984] performed better than the ANFIS with RMSE of?1.61 and R ² of 0.965. The mentioned results prove the superiority of the newly developed PSO–ANFIS model in estimating blast-produced ground vibrations.     

小秦岭金矿带潼峪矿集区金的成矿规律与找矿预测

在调查分析的基础上,结合变质核杂岩构造特征,研究了小秦岭金矿带潼峪矿集区金的成矿规律与控矿因素,指出潼峪矿集区金矿床的成矿物质来自太华群,并受晚期岩浆和构造的控制,尤其是变质核杂岩构造系统及其剥离(滑脱)构造控矿特征非常明显。综合研究认为,区内佛头崖-狮子包地段、东交沟—佛头崖一带、岳王庙-王排沟地区与复背斜倾伏端是重要的找矿靶区,值得进一步深入工作,并可为老矿区找矿提供示范。这对在老矿区深部寻找金矿,把小秦岭西段的深部找矿工作推向新阶段,实现找矿突破,具有重要的现实意义。     

延边中西部地区钼矿成矿地质特征

本文通过延边中西部地区已知钼矿床(点)时空分布规律及区域成矿地质条件以及对具有代表性矿床(点)的野外实际调研工作。从钼矿与断裂、岩体、围岩蚀变等方面的关系揭示了钼矿成矿的基本特征;从钼矿的矿化类型、空间分布规律等方面论述了钼矿的矿化特征。对该区的钼矿勘查工作具有一定的指导意义。     

Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco,Italy

This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen predisposing factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system. The used predictive models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and predisposing factors. Then, landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC?=?0.916) presented the most accurate map, followed by the ANFIS (AUC?=?0.889) and FR (AUC?=?0.888). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of predisposing factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

     

Geology and Gold Mineralization in the Pan-African Rocks of the Adola Area, Southern Ethiopia

Primary and placer gold deposits are mined from the Pan-African Adola volcano-sedimentary sequence, in southern Ethiopia. Two major mineralized belts can be recognized: the Megado (‘Gold Belt’) and the Kenticha Belts. The Kenticha Belt is also known for its rare metal mineralization. Extensive exploration of the area resulted in two most important primary gold deposits of Lega Dembi and Sakaro. The primary gold deposits are classified into four classes based on their geological setting:

- auriferous veins, lodes, stockworks and silicified zones disseminated in schistose rocks

- gold associated with quartzite

- gold mineralization confined to conglomerates and meta-arkoses

- auriferous quartz veins in high grade gneiss rocks

This classification provides a useful guide for future exploration programme     

遥感技术在多龙铜矿预测区成矿预测中的应用

多龙铜矿集区位于班公湖～怒江成矿带西段,区内已知的典型矿床为多不杂和波龙斑岩型铜矿,是大型的铜多金属矿床,其成矿背景复杂,找矿潜力巨大。利用ETM+遥感影像,对研究区进行构造解译和矿化蚀变信息提取的工作,再结合区域地质、物探、化探等信息进行综合找矿分析,并结合已知矿床点,圈定出二处遥感找矿远景区,为矿产资源潜力评价和成矿预测提供了重要的信息和依据。     

黑龙江省某地区钼、铅锌多金属矿(化)点地质特征及找矿标志

研究区位于小兴安岭—张广才岭多金属成矿带之翠宏山—二股铁、有色金属成矿亚带(也称铁力—玉泉有色金属、贵金属成矿带)的中南段,呈南北向带状展布。成矿带中区域上发育三期成矿作用,加里东中期成矿作用、印支晚期成矿作用和燕山期成矿作用,是黑龙江省重要的铅、锌、银、铁、钨、钼等多金属成矿带。测区先后发现了前进东山铅锌矿点、前进先锋沟铁矿化点、牛奶沟钼矿点、九小工队钼矿化点。本文以牛奶沟钼矿点、九小工队钼矿化点为例总结了五三零和寒月林场地区钼、铅锌多金属矿化点的地质特征并确定了其成矿规律与找矿标志。     

Comprehensive modeling of mineral potential mapping by integration of multiset geosciences data

This work has evaluated the functionality of various fuzzy-based fusion methods in the mineral potential mapping (MPM), by which a multi-criteria decision-making problem was solved to design a layout for drilling complementary boreholes through a comprehensive analysis of geospatial datasets. The novel methods employed were fuzzy c-means clustering, fuzzy gamma operator, fuzzy inference system (FIS), fuzzy outranking, and fuzzy ordered weighted averaging (FOWA). Kahang porphyry Cu-Mo deposit in the Isfahan province of Iran was chosen as a case study to examine the performance of these fuzzy methods in MPM. Extracted geospatial indicator layers for assessing the potential of porphyry-type mineralization were derived from four criteria, namely geology (rock units and faults), remote sensing (alteration map), geochemistry (Cu, Mo, and factor maps), and geophysics (reduced to the pole and analytical signal of magnetic data). The concentration-area multifractal method was utilized to reclassify each synthesized fuzzy favorability map into five classes. To appraise and compare the efficiency of each employed method, a productivity measure assumed as a cumulative summation of Cu grade multiplied by its thickness above an economical cut-off value of 0.2% was calculated along with each drilling (totally 33 ones). According to fuzzy favorability maps derived from running all fuzzy methods, the FIS and FOWA had the highest efficiency with 80 and 78% of accuracy, respectively. Eventually, taking all fuzzy maps into account led to the delineation of some new favorable zones, whereby further exploratory investigations are envisioned for determining their mining potential.