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.     

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.

     

Geodata Science-Based Mineral Prospectivity Mapping: A Review

Natural Resources Research - This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on analyzing the spatial associations between...     

Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox

With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.

     

Mineral Prospectivity Mapping via Gated Recurrent Unit Model

Natural Resources Research - The application of deep learning algorithms in mineral prospectivity mapping (MPM) is a hot topic in mineral exploration. However, few studies have focused on recurrent...     

Effects of Random Negative Training Samples on Mineral Prospectivity Mapping

Natural Resources Research - Supervised data-driven mineral prospectivity mapping (MPM) usually employs both positive and negative training datasets. Positive training datasets are typically...     

Data-Driven Index Overlay and Boolean Logic Mineral Prospectivity Modeling in Greenfields Exploration

Index overlay and Boolean logic are two techniques customarily applied for knowledge-driven modeling of prospectivity for mineral deposits, whereby weights of values in evidential maps and weights of every evidence map are assigned based on expert opinion. In the Boolean logic technique for mineral prospectivity modeling (MPM), threshold evidential values for creating binary maps are defined based on expert opinion as well. This practice of assigning weights based on expert opinion involves trial-and-error and introduces bias in evaluating relative importance of both evidential values and individual evidential maps. In this paper, we propose a data-driven index overlay MPM technique whereby weights of individual evidential maps are derived from data. We also propose a data-driven Boolean logic MPM technique, whereby thresholds for creating binary maps are defined based on data. For assigning weights and defining thresholds in these proposed data-driven MPM techniques, we applied a prediction-area plot from which we can estimate the predictive ability of each evidential map with respect to known mineral occurrences, and we use that predictive ability estimate to assign weights to evidential map and to select thresholds for generating binary predictor maps. To demonstrate these procedures, we applied them to an area in the Kerman province in southeast Iran as a MPM case study for porphyry-Cu deposits.     

Determination of Predictive Variables in Mineral Prospectivity Mapping Using Supervised and Unsupervised Methods

Natural Resources Research - Machine learning methods have recently been used widely for mineral prospectivity mapping. However, few studies have focused on the determination of variables for...     

Mineral Prospectivity Prediction from High-Dimensional Geoscientific Data Using a Similarity-Based Density Estimation Model

Assuming a study region in which each cell has associated with it an N-dimensional vector of values corresponding to N predictor variables, one means of predicting the potential of some cell to host mineralization is to estimate, on the basis of historical data, a probability density function that describes the distribution of vectors for cells known to contain deposits. This density estimate can then be employed to predict the mineralization likelihood of other cells in the study region. However, owing to the curse of dimensionality, estimating densities in high-dimensional input spaces is exceedingly difficult, and conventional statistical approaches often break down. This article describes an alternative approach to estimating densities. Inspired by recent work in the area of similarity-based learning, in which input takes the form of a matrix of pairwise similarities between training points, we show how the density of a set of mineralized training examples can be estimated from a graphical representation of those examples using the notion of eigenvector graph centrality. We also show how the likelihood for a test example can be estimated from these data without having to construct a new graph. Application of the technique to the prediction of gold deposits based on 16 predictor variables shows that its predictive performance far exceeds that of conventional density estimation methods, and is slightly better than the performance of a discriminative approach based on multilayer perceptron neural networks.     

Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite,Nunavut, Canada

This paper outlines the process taken to create two separate gold prospectivity maps. The first was created using a combination of several knowledge-driven (KD) techniques. The second was created using a relatively new classification method called random forests (RF). The purpose of this study was to examine the results of the RF technique and to compare the results to that of the KD model. The datasets used for the creation of evidence maps for the gold prospectivity mapping include a comprehensive lake sediment geochemical dataset, interpreted geological structures (form lines), mapped and interpreted faults, lithology, topographic features (lakes), and known Au occurrences. The RF method performed well in that the gold prospectivity map created was a better predictor of the known Au occurrences than the KD gold prospectivity map. This was further validated by a fivefold repetition using a subset of the input training areas. Several advantages to the use of RF include (1) the ability to take both continuous and/or categorical data as variable inputs, (2) an internal, unbiased estimation of the mapping error (out-of-bag error) removing the need for a cross-validation of the final outputs to determine accuracy, and (3) the estimation of importance of each input variable. Efficiency of prediction curves illustrates that the RF method performs better than the KD method. The success rate is significantly higher for the RF method than for the KD method.     

A Convolutional Neural Network of GoogLeNet Applied in Mineral Prospectivity Prediction Based on Multi-source Geoinformation

Natural Resources Research - The traditional convolutional neural networks applied in mineral prospectivity mapping usually extract features from only one scale at each iteration, resulting in...     

Machine Learning-Based 3D Modeling of Mineral Prospectivity Mapping in the Anqing Orefield,Eastern China

Natural Resources Research - Successful delineation of high potential targets for exploration in maturely-explored orefields is still a tough challenge. A reliable prediction model achieved by...     

Uncertainties in GIS-Based Mineral Prospectivity Mapping: Key Types,Potential Impacts and Possible Solutions

Natural Resources Research - GIS-based mineral prospectivity mapping (MPM) is a computer-aided methodology for delineating and better constraining target areas deemed prospective for mineral...     

Isolation Forest as an Alternative Data-Driven Mineral Prospectivity Mapping Method with a Higher Data-Processing Efficiency

Natural Resources Research - Mineral exploration targets can be delineated through multivariate analysis. These targets are usually recognized as anomalies in the procedure of data mining using a...     

African Neoproterozoic Mineral Deposits and Pan African Metallogenesis

Amalgamation of a number of continental fragments during the Late Neoproterozoic resulted in a united Gondwana continent. The time period in question, at the end of the Precambrian, spans about 250 million years between ∼800 and 550 Ma. Geological activity focused along orogenic belts in Africa during that time period is generally referred to as “Pan African.” We identify three age-related classes of tectonic terranes within these orogenic belts, differentiated on the basis of the formation-age of their crust: juvenile (e.g. mantle derived at or near the time of the orogenesis, ∼0.5–0.8 Ga), Paleoproterozoic (∼1.8–2.5 Ga), Archean (>2.5 Ga). We combine African mineral deposits data of these terranes on a new Neoproterozoic tectonic map of Africa. The spatial correlation between geological terranes in the belts and mineral occurrences are determined in order to define the metallogenic character of each terrane, which we refer to as their “metallogenic fingerprint.” We use these fingerprints to evaluate the effectiveness of mobilization (“recycling”) of mineral deposits within old crustal fragments during Pan African orogenesis. This analysis involves normalization factors derived from the average metallogenic fingerprints of pristine older crust (e.g. Palaeoproterozoic shields and Archean cratons not affected by Pan African orogenesis) and of juvenile Pan African crust (e.g. the Nubian Shield). We find that mineral deposit patterns are distinctly different in older crust that has been remobilized in the Pan African belts compared to those in juvenile crust of Neoproterozoic age, and that the concentration of deposits in remobilized older crust is in all cases significantly depleted relative to that in their pristine age-equivalents. Lower crustal sections (granulite domains) within the Pan African belts are also strongly depleted in mineral deposits relative to the upper crustal sections of juvenile Neoproterozoic terranes. A depletion factor for all terranes in Pan African orogens is derived with which to evaluate the role of mineral deposit recycling during orogenesis. We conclude that recycling of old mineral deposits in younger orogenic belts contributes, on average, to secular decrease of the total mineral endowment of continental crust. This could be of value when formulating exploration strategies.     

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.     

Geochemical Anomaly and Mineral Prospectivity Mapping for Vein-Type Copper Mineralization,Kuhsiah-e-Urmak Area,Iran: Application of Sequential Gaussian Simulation and Multivariate Regression Analysis

Natural Resources Research - In this paper, sequential Gaussian simulation (SGS) and number–size (N–S) fractal modeling were used for copper geochemical anomaly mapping in the western...     

Assessment of Various Fuzzy c-Mean Clustering Validation Indices for Mapping Mineral Prospectivity: Combination of Multifractal Geochemical Model and Mineralization Processes

This paper describes the application of an unsupervised clustering method, fuzzy c-means (FCM), to generate mineral prospectivity models for Cu?±?Au?±?Fe mineralization in the Feizabad District of NE Iran. Various evidence layers relevant to indicators or potential controls on mineralization, including geochemical data, geological–structural maps and remote sensing data, were used. The FCM clustering approach was employed to reduce the dimensions of nine key attribute vectors derived from different exploration criteria. Multifractal inverse distance weighting interpolation coupled with factor analysis was used to generate enhanced multi-element geochemical signatures of areas with Cu?±?Au?±?Fe mineralization. The GIS-based fuzzy membership function MSLarge was used to transform values of the different evidence layers, including geological–structural controls as well as alteration, into a [0–1] range. Four FCM-based validation indices, including Bezdek’s partition coefficient (V_Pc) and partition entropy (V_Pe) indices, the Fukuyama and Sugeno (V_FS) index and the Xie and Beni (V_XB) index, were employed to derive the optimum number of clusters and subsequently generate prospectivity maps. Normalized density indices were applied for quantitative evaluation of the classes of the FCM prospectivity maps. The quantitative evaluation of the results demonstrates that the higher favorability classes derived from V_FS and V_XB (N_d?=?9.19) appear more reliable than those derived from V_Pc and V_Pe (N_d?=?6.12) in detecting existing mineral deposits and defining new zones of potential Cu?±?Au?±?Fe mineralization in the study area.

     

Data-driven Mineral Prospectivity Mapping by Joint Application of Unsupervised Convolutional Auto-encoder Network and Supervised Convolutional Neural Network

Natural Resources Research - The excellent performance of convolutional neural network (CNN) and its variants in image classification makes it a potential perfect candidate for dealing with...