Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area,Southern Spain

Mineral exploration activities require robust predictive models that result in accurate mapping of the probability that mineral deposits can be found at a certain location. Random forest (RF) is a powerful machine data-driven predictive method that is unknown in mineral potential mapping. In this paper, performance of RF regression for the likelihood of gold deposits in the Rodalquilar mining district is explored. The RF model was developed using a comprehensive exploration GIS database composed of: gravimetric and magnetic survey, a lithogeochemical survey of 59 elements, lithology and fracture maps, a Landsat 5 Thematic Mapper image and gold occurrence locations. The results of this study indicate that the use of RF for the integration of large multisource data sets used in mineral exploration and for prediction of mineral deposit occurrences offers several advantages over existing methods. Key advantages of RF include: (1) the simplicity of parameter setting; (2) an internal unbiased estimate of the prediction error; (3) the ability to handle complex data of different statistical distributions, responding to nonlinear relationships between variables; (4) the capability to use categorical predictors; and (5) the capability to determine variable importance. Additionally, variables that RF identified as most important coincide with well-known geologic expectations. To validate and assess the effectiveness of the RF method, gold prospectivity maps are also prepared using the logistic regression (LR) method. Statistical measures of map quality indicate that the RF method performs better than LR, with mean square errors equal to 0.12 and 0.19, respectively. The efficiency of RF is also better, achieving an optimum success rate when half of the area predicted by LR is considered.     

西藏盐湖矿产资源遥感定量预测方法研究

西藏自治区地域广大,湖泊众多,盐湖矿产资源十分丰富,但调查研究程度较低,资源潜力不明,家底不清。以遥感信息为基础,采用多因素综合评判模型法进行盐湖矿产定量预测,初步摸清现阶段西藏盐湖矿产资源家底,为地方政府和有关部门进行盐湖矿产资源勘查开发提供了重要参考依据。该预测方法具有较强探索性,和已知查明资源量进行比较,预测结果基本可靠,是西部高海拔地区盐湖矿产资源快速评价的有效方法。     

An AHP–TOPSIS Predictive Model for District-Scale Mapping of Porphyry Cu–Au Potential: A Case Study from Salafchegan Area (Central Iran)

The Salafchegan area in central Iran is a greenfield region of high porphyry Cu–Au potential, for which a sound prospectivity model is required to guide mineral exploration. Satellite imagery, geological geochemical, geophysical, and mineral occurrence datasets of the area were used to run an innovative integration model for porphyry Cu–Au exploration. Five favorable multi-class evidence maps, representing diagnostic porphyry Cu–Au recognition criteria (intermediate igneous intrusive and sub-volcanic host rocks, structural controls, hydrothermal alterations, stream sediment Cu anomalies, magnetic signatures), were combined using analytic hierarchy process and technique for order preference by similarity to ideal solution to calculate a final map of porphyry Cu–Au potential in the Salafchegan area.     

Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects,Abra, Philippines

Weights of evidence (WofE) modeling usually is applied to map mineral potential in areas with large number of deposits/prospects. In this paper, WofE modeling is applied to a case study area measuring about 920 km² with 12 known porphyry copper prospects. A pixel size of 100 m × 100 m was used in the spatial data analyses to represent in a raster-based GIS lateral extents of prospects and of geological features considered as spatial evidence. Predictor maps were created based on (a) estimates of studentized values of positive spatial association between prospects and spatial evidence; (b) proportion of number of prospects in zones where spatial evidence is present; and (c) geological interpretations of positive spatial association between prospects and spatial evidence. Uncertainty because of missing geochemical evidence is shown to have an influence on tests of assumption of conditional independence (CI) among predictor maps with respect to prospects. For the final predictive model, assumption of CI is rejected based on omnibus test but is accepted based on a new omnibus test. The final predictive model, which delineates 30% of study area as zones with potential for porphyry copper, has 83% success rate and 73% prediction rate. The results demonstrate plausibility of WofE modeling of mineral potential in large areas with small number of mineral prospects.     

Quantitative Prediction and Evaluation of Mineral Resources Based on GIS: A Case Study in Sanjiang Region,Southwestern China

Quantitative prediction and evaluation of mineral resources are one of the important topics of mathematical geology. On the basis of GIS technologies and weights of evidence modeling, MapGIS is integrated with GIS and mineral-resource prediction and evaluation. The final product is a predictor map of posterior probabilities of occurrence of the discrete event within a small unit cell. Predictor layers were created on a digital database that includes 1:200,000 scale geological, and geochemical, and geophysical maps, and remote-sensing images in study area. According to metallogenetic factors extractiont and weights of evidence modeling, there are four main metal ore belts in the study area: (1) the Batang belt; (2) the Lei Wuqi belt; (3) the Basu-Chayu belt; and (4) the Ganzi-Litang belt. The predictor map of posterior probabilities show that 29% of study area as zones with potential for porphyry copper, and 81% known mineral occurrences success rate is circled in the metallogenetic posterior probabilities map. The results demonstrate plausibility of weights-of-evidence modeling of mineral potential in large areas with small number of mineral prospects.     

Enhancement and Mapping of Weak Multivariate Stream Sediment Geochemical Anomalies in Ahar Area,NW Iran

In this study, stream sediment geochemical data have been subjected to robust principal components analysis (RPCA) and singularity mapping (SM) to enhance and map significant multivariate geochemical anomalies (i.e., mineralization-related) in Ahar area, NW Iran. The RPCA was applied to (a) account for the compositional nature of stream sediment geochemical data using suitable log-ratio transformation, (b) modulate the effect of outliers in component estimation and (c) derive a multivariate geochemical footprint of mineralization. The SM was applied to extract anomalous patterns of the multivariate geochemical footprint of mineralization. The exploration targets were then delineated using Student’s t-statistics analysis. The correlations of mapped exploration targets with the known mineral occurrences and mineralization-related patterns were further evaluated using normalized density index and overall accuracy analyses.     

土壤地球化学测量在辽宁省开原市小甸子银金多金属矿区的找矿效果

在勘查区内通过1∶10 000土壤地球化学测量工作发现多处异常,结合区内成矿地质条件及矿种属性可将异常划分为3个异常区带,即西部HT1、东部HT2、南部HT3。经地表探槽工程揭露部分异常与已知矿体较为吻合,并且在勘查区东部HT2-3异常带内新发现4条蚀变岩型Zn矿体。土壤地球化学测量在该区地质找矿中发挥了重要的指导作用,取得了良好的找矿效果。     

Estimation of undiscovered deposits in quantitative mineral resource assessments—examples from Venezuela and Puerto Rico

Quantitative mineral resource assessments used by the United States Geological Survey are based on deposit models. These assessments consist of three parts: (1) selecting appropriate deposit models and delineating on maps areas permissive for each type of deposit; (2) constructing a grade-tonnage model for each deposit model; and (3) estimating the number of undiscovered deposits of each type. In this article, I focus on the estimation of undiscovered deposits using two methods: the deposit density method and the target counting method.In the deposit density method, estimates are made by analogy with well-explored areas that are geologically similar to the study area and that contain a known density of deposits per unit area. The deposit density method is useful for regions where there is little or no data. This method was used to estimate undiscovered low-sulfide gold-quartz vein deposits in Venezuela.Estimates can also be made by counting targets such as mineral occurrences, geophysical or geochemical anomalies, or exploration plays and by assigning to each target a probability that it represents an undiscovered deposit that is a member of the grade-tonnage distribution. This method is useful in areas where detailed geological, geophysical, geochemical, and mineral occurrence data exist. Using this method, porphyry copper-gold deposits were estimated in Puerto Rico.     

Spatial Analysis Techniques as Successful Mineral-Potential Mapping Tools for Orogenic Gold Deposits in the Northern Fennoscandian Shield,Finland

Geoscientific Information Systems (GIS) provide tools to quantitatively analyze and integrate spatially referenced information from geological, geophysical, and geochemical surveys for decision-making processes. Excellent coverage of well-documented, precise and good quality data enables testing of variable exploration models in an efficient and cost effective way with GIS tools. Digital geoscientific data from the Geological Survey of Finland (GTK) are being used widely as spatial evidence in exploration targeting, that is ranking areas based on their exploration importance. In the last few years, spatial analysis techniques including weights-of-evidence, logistic regression, and fuzzy logic, have been increasingly used in GTK’s mineral exploration and geological mapping projects. Special emphasis has been put into the exploration for gold because of the excellent data coverage within the prospective volcanic belts and because of the increased activity in gold exploration in Finland during recent years. In this paper, we describe some successful case histories of using the weights-of-evidence method for the Au-potential mapping. These projects have shown that, by using spatial modeling techniques, exploration targets can be generated by quantitatively analyzing extensive amounts of data from various sources and to rank these target areas based on their exploration potential.     

A Bat Algorithm-Based Data-Driven Model for Mineral Prospectivity Mapping

In data-driven mineral prospectivity mapping, a statistical model is established to represent the spatial relationship between layers of metallogenic evidence and locations of known mineral deposits, and then, the former are integrated into a mineral prospectivity model using the established model. Establishment of a data-driven mineral prospectivity model can be regarded as a process of searching for the optimal integration of layers of metallogenic evidence in order to maximize the spatial relationship between mineral prospectivity and the locations of known mineral deposits. Mineral prospectivity can be simply defined as the weighted sum of layers of metallogenic evidence. Then, the optimal integration of the layers of evidence can be determined by optimizing weight coefficients of the layers of evidence to maximize the area under the curve (AUC) of the defined model. To this end, a bat algorithm-based model is proposed for data-driven mineral prospectivity mapping. In this model, the AUC of the model is used as the objective function of the bat algorithm, and the ranges of the weight coefficients of layers of evidence are used to define the search space of the bat population, and the optimal weight coefficients are then automatically determined through the iterative search process of the bat algorithm. The bat algorithm-based model was used to map mineral prospectivity in the Helong district, Jilin Province, China. Because of the high performance of the traditional logistic regression model for data-driven mineral prospectivity mapping, it was used as a benchmark model for comparison with the bat algorithm-based model. The result shows that the receiver operating characteristic (ROC) curve of the bat algorithm-based model is coincident with that of the logistic regression model in the ROC space. The AUC of the bat algorithm-based model (0.88) is slightly larger than that of the logistic regression model (0.87). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets optimally delineated by using the bat algorithm-based model and logistic regression model account for 8.10% and 11.24% of the study area, respectively, both of which contain 79% of the known mineral deposits. These results indicate that the performance of the bat algorithm-based model is comparable with that of the logistic regression model in data-driven mineral prospectivity mapping. Therefore, the bat algorithm-based model is a potentially useful high-performance data-driven mineral prospectivity mapping model.

     

现代盐湖型钾盐成矿遥感预测——以扎布耶盐湖为例

通过遥感图像处理与目视解译,对扎布耶盐湖典型矿床从控矿构造、矿物质来源、湖盆封闭性和沉降性、地形地貌、水热活动、遥感色异常及退宿湖痕迹等因素进行了总结,初步建立主要盐类矿产资源的遥感找矿模式。经过综合分析,对比扎布耶盐湖典型矿床的成矿地质条件,以近矿找矿标志及遥感成矿信息为依据圈定了24个成矿条件优越、遥感找矿标志明显的区域为钾盐矿最小预测区。该研究可为矿产潜力评价及以后的工作奠定基础。     

Predictive Mapping of Prospectivity in the Gurupi Orogenic Gold Belt,North–Northeast Brazil: An Example of District-Scale Mineral System Approach to Exploration Targeting

The Gurupi Belt hosts a Paleoproterozoic gold province located in north–northeastern Brazil, at the borders of Pará and Maranhão states. It is considered to be an extension of the prolific West African Craton’s Birimian gold province into South America. Additionally, the belt has been the object of recent mineral exploration programs with significant resource discoveries. This study presents the results of predictive mapping using up-to-date mineral system concepts and recently finished regional-scale geological mapping, stream sediment and airborne geophysical surveys conducted by the Geological Survey of Brazil. We relate gold mineralization to an initially enriched crust, metamorphism, deep fluid pathways, structurally controlled damage zones and hydrothermal alteration. Prospective targets were generated using only regional public datasets and knowledge-driven targeting technique. This work did not incorporate any known gold deposits, yet it predicted the largest known deposits and their satellite targets. Besides, high prospective targets mapped almost 40% of known primary gold occurrences within 7% of the project area. This work allowed considerable search area reduction and identification of new target areas, thus collaborating on reducing costs, time and risk of mineral exploration. Results indicate that we achieved an efficient understanding of the geological processes related to the Gurupi Belt mineral system.     

基于晕状特征提取的化探异常信息识别及其在成矿预测中的应用

常规化探异常信息识别通常都是通过对比观测值与某一异常阈值的高低来判定某样品是否为异常样品,很多方法或者建立在经典统计学基础之上,要求数据符合一定的分布形式,或者面向整个研究区计算异常阈值,而无法顾及实际的地质环境。根据常规方法以数值大小计算异常阈值的原则,并且关注化探数据分布特征信息的分析和挖掘,提出了晕状特征提取方法,该方法能够有效识别局部异常及低缓异常。将此方法用于克拉玛依地区对金矿预测具有指示意义的化探数据的异常信息识别工作,结果表明:该方法能够有效识别化探异常信息,这些异常信息与研究区内已知金矿具有很好的对应关系。晕状特征提取方法在新疆东部的应用案例也显示出较好的结果。该方法可以作为一种有效的化探异常信息识别方法应用于成矿预测实际工作中。     

Fractal pattern integration for mineral potential estimation

Concepts of fractal/multifractal dimensions and fractal measure were used to derive the prior and posterior probabilities that a small unit cell on a geological map contains one or more mineral deposits. This has led to a new version of the weights of evidence technique which is proposed for integrating spatial datasets that exhibit nonfractal and fractal patterns to predict mineral potential. The method is demonstrated with a case study of gold mineral potential estimation in the Iskut River area, northwestern British Columbia. Several geological, geophysical, and geochemical patterns (Paleozoic-Mesozoic sedimentary and volcanic clastic rocks; buffer zones around the contacts between sedimentary rocks and Mesozoic intrusive rocks; a linear magnetic anomaly; and geochemical anomalies for Au and associated elements in stream sediments) were integrated with the gold mineral occurrences which have fractal and multifractal properties with a box-counting dimension of 1.335±0.077 and cluster dimension of 1.219±0.037.     

Mineral Systems Analysis and Artificial Neural Network Modeling of Chromite Prospectivity in the Western Limb of the Bushveld Complex,South Africa

Radial basis function link neural network (RBFLN) and fuzzy-weights of evidence (fuzzy-WofE) methods were used to assess regional-scale prospectivity for chromite deposits in the Western Limb and the Nietverdiend layered mafic intrusion of the Bushveld Complex in South Africa. Five predictor maps derived from geological, geochemical and geophysical data were processed in a GIS environment and used as spatial proxy for critical processes that were most probably responsible for the formation of the chromite deposits in the study area. The RBFLN was trained using input feature vectors that correspond to known deposits, prospects and non-deposits. The training was initiated by varying the number of radial basis functions (RBFs) and iterations. The results of training the RBFLN provided optimum number of RBFs and iterations that were used for classification of the input feature vectors. The results show that the network classified 73% of the validation deposits into highly prospective areas for chromite deposit, covering 6.5% of the study area. The RBFLN entirely classified all the non-deposit validation points into low prospectivity areas, occupying 86.6% of the study area. In general, the efficiency of the RBFLN in classifying the validation deposits and non-deposits indicates the degree of spatial relationship between the input feature vectors and the training points, which represent chrome mines and prospects. The RBFLN and fuzzy-WofE analyses used in this study are important in guiding identification of regional-scale prospect areas where further chromite exploration can be carried out.     

Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space

Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.

     

Application of Mineral Exploration Models and GIS to Generate Mineral Potential Maps as Input for Optimum Land-Use Planning in the Philippines

The inherent problems of classifying or inventorying potential mineral resources (as opposed to known mineral resources) pose specific challenges. In this paper, the application of a conceptual mineral exploration model and GIS to generate mineral potential maps as input to land-use policy decision-making is illustrated. We implement the criteria provided by a conceptual exploration model for nickeliferous-laterites by using a GIS to classify the nickeliferous-laterite potential of an area in the northeastern part of the Philippines. The spatial data inputs to the GIS are geological map data, topographic map data, and stream sediment point data. Processing of these data yields derivative maps, which are used as indicators of nickeliferous-laterite potential. The indicator maps then are integrated to furnish a nickeliferous-laterite potential map. This map is compared with present land-use classification and policy in the area. The results indicate high potential for nickeliferous-laterite occurrence in the area, but the zones of potential are in places where mineral resources development is prohibited. The prohibition was imposed before the nickeliferous-laterite potential was assessed by this study. Mineral potential classification therefore is a critical input to land-use policy-making so that prospective land is not alienated from future mineral resource development.     

Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation

Geological disasters not only cause economic losses and ecological destruction, but also seriously threaten human survival. Selecting an appropriate method to evaluate susceptibility to geological disasters is an important part of geological disaster research. The aims of this study are to explore the accuracy and reliability of multi-regression methods for geological disaster susceptibility evaluation, including Logistic Regression (LR), Spatial Autoregression (SAR), Geographical Weighted Regression (GWR), and Support Vector Regression (SVR), all of which have been widely discussed in the literature. In this study, we selected Yunnan Province of China as the research site and collected data on typical geological disaster events and the associated hazards that occurred within the study area to construct a corresponding index system for geological disaster assessment. Four methods were used to model and evaluate geological disaster susceptibility. The predictive capabilities of the methods were verified using the receiver operating characteristic (ROC) curve and the success rate curve. Lastly, spatial accuracy validation was introduced to improve the results of the evaluation, which was demonstrated by the spatial receiver operating characteristic (SROC) curve and the spatial success rate (SSR) curve. The results suggest that: 1) these methods are all valid with respect to the SROC and SSR curves, and the spatial accuracy validation method improved their modelling results and accuracy, such that the area under the curve (AUC) values of the ROC curves increased by about 3%–13% and the AUC of the success rate curve values increased by 15%–20%; 2) the evaluation accuracies of LR, SAR, GWR, and SVR were 0.8325, 0.8393, 0.8370 and 0.8539, which proved the four statistical regression methods all have good evaluation capability for geological disaster susceptibility evaluation and the evaluation results of SVR are more reasonable than others; 3) according to the evaluation results of SVR, the central-southern Yunnan Province are the highest susceptibility areas and the lowest susceptibility is mainly located in the central and northern parts of the study area.     

A MaxEnt Model for Mineral Prospectivity Mapping

Mineral prospectivity mapping is an important preliminary step for mineral resource exploration. It has been widely applied to distinguish areas of high potential to host mineral deposits and to minimize the financial risks associated with decision making in mineral industry. In the present study, a maximum entropy (MaxEnt) model was applied to investigate its potential for mineral prospectivity analysis. A case study from the Nanling tungsten polymetallic metallogenic belt, South China, was used to evaluate its performance. In order to deal with model over-fitting, varying levels of β _j -regularization were set to determine suitable β value based on response curves and receiver operating characteristic (ROC) curves, as well as via visual inspections of prospectivity maps. The area under the ROC curve (AUC = 0.863) suggests good performance of the MaxEnt model under the condition of balancing model complexity and generality. The relative importance of ore-controlling factors and their relationships with known deposits were examined by jackknife analysis and response curves. Prediction–area (P–A) curves were used to determine threshold values for demarcating high probability of tungsten polymetallic deposit occurrence within small exploration area. The final predictive map showed that high favorability zones occupy 14.5% of the study area and contain 85.5% of the known tungsten polymetallic deposits. Our study suggests that the MaxEnt model can be efficiently used to integrate multisource geo-spatial information for mineral prospectivity analysis.     

罗布泊罗北西3号断陷带固体钾盐矿地质特征及成因分析

经遥感解译及地表调查,在罗布泊新庆台地罗北西3号断陷带内存在固体钾盐矿化带。为初步查明该断陷带钾盐矿化带的矿体赋存特征、矿物组分、矿石类型及矿体规模,开展了系统的坑探揭露、刻槽取样、盐矿鉴定、X射线衍射及化学分析测试等工作。经勘查,钾盐矿层赋存于洼地内第四系全新统化学沉积层中,呈似层状分布。面积约30 km²,矿层厚0.30～2.73 m,平均1.02 m。经X射线衍射及盐矿鉴定,矿石中含钾矿物以杂卤石为主,钾盐镁矾次之,局部见含光卤石,矿石中KCl含量在2.07%～8.44%,平均含量在3.55%。通过本次研究,确认该区存在一低品位固体钾盐矿床,初步查明了矿床地质特征,其成因为台地内的局部洼地汇水经蒸发作用沉积形成,为进一步的勘查及开发提供了地质依据。