Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines

Binary predictor patterns of geological features are integrated based on a probabilistic approach known as weights of evidence modeling to predict gold potential. In weights of evidence modeling, the log _e of the posterior odds of a mineral occurrence in a unit cell is obtained by adding a weight, W ⁺ or W ^– for presence of absence of a binary predictor pattern, to the log _e of the prior probability. The weights are calculated as log _e ratios of conditional probabilities. The contrast, C = W ⁺ – W ^–, provides a measure of the spatial association between the occurrences and the binary predictor patterns. Addition of weights of the input binary predictor patterns results in an integrated map of posterior probabilities representing gold potential. Combining the input binary predictor patterns assumes that they are conditionally independent from one another with respect to occurrences.     

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.     

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.     

A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping

This paper describes a hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping that generates fuzzy predictor patterns based on (a) knowledge-based fuzzy membership values and (b) data-based conditional probabilities. The fuzzy membership values are calculated using a knowledge-driven logistic membership function, which provides a framework for treating systemic uncertainty and also facilitates the use of multiclass predictor maps in the modeling procedure. The fuzzy predictor patterns are combined using Bayes’ rule in a log-linear form (under an assumption of conditional independence) to update the prior probability of target deposit-type occurrence in every unique combination of predictor patterns. The hybrid fuzzy WofE model is applied to a regional-scale mapping of base-metal deposit potential in the south-central part of the Aravalli metallogenic province (western India). The output map of fuzzy posterior probabilities of base-metal deposit occurrence is classified subsequently to delineate zones with high-favorability, moderate favorability, and low-favorability for occurrence of base-metal deposits. An analysis of the favorability map indicates (a) significant improvement of probability of base-metal deposit occurrence in the high-favorability and moderate-favorability zones and (b) significant deterioration of probability of base-metal deposit occurrence in the low-favorability zones. The results demonstrate usefulness of the hybrid fuzzy WofE model in representation and in integration of evidential features to map relative potential for mineral deposit occurrence.     

Developing a geographic expert system for regional mapping of volcanogenic massive sulfide (VMS) deposit potential

A personal computer-based geographic information system (GIS) is used to develop a geographic expert system (GES) for mapping and evaluating volcanogenic massive sulfide (VMS) deposit potential. The GES consists of an inference network to represent expert knowledge, and a GIS to handle the spatial analysis and mapping. Evidence from input maps is propagated through the inference network, combining information by means of fuzzy logic and Bayesian updating to yield new maps showing evaluation of hypotheses. Maps of evidence and hypotheses are defined on a probability scale between 0 and 1. Evaluation of the final hypothesis results in a mineral potential map, and the various intermediate hypotheses can also be shown in map form.The inference net, with associated parameters for weighting evidence, is based on a VMS deposit model for the Chisel Lake deposit, a producing mine in the Early Protoerzoic Snow Lake greenstone belt of northwest Manitoba. The model is applied to a small area mapped at a scale of 1:15,840. The geological map, showing lithological and alteration units, provides the basic input to the model. Spatial proximity to contacts of various kinds are particularly important. Three types of evidence are considered: stratigraphic, heat source, and alteration. The final product is a map showing the relative favorability for VMS deposits. The model is implemented as aFortran program, interfaced with the GIS. The sensitivity of the model to changes in the parameters is evaluated by comparing predicted areas of elevated potential with the spatial distribution of known VMS occurrences.     

Fuzzy Weights of Evidence Method and Its Application in Mineral Potential Mapping

This paper proposes a new approach of weights of evidence method based on fuzzy sets and fuzzy probabilities for mineral potential mapping. It can be considered as a generalization of the ordinary weights of evidence method, which is based on binary or ternary patterns of evidence and has been used in conjunction with geographic information systems for mineral potential mapping during the past few years. In the newly proposed method, instead of separating evidence into binary or ternary form, fuzzy sets containing more subjective genetic elements are created; fuzzy probabilities are defined to construct a model for calculating the posterior probability of a unit area containing mineral deposits on the basis of the fuzzy evidence for the unit area. The method can be treated as a hybrid method, which allows objective or subjective definition of a fuzzy membership function of evidence augmented by objective definition of fuzzy or conditional probabilities. Posterior probabilities calculated by this method would depend on existing data in a totally data-driven approach method, but depend partly on expert's knowledge when the hybrid method is used. A case study for demonstration purposes consists of application of the method to gold deposits in Meguma Terrane, Nova Scotia, Canada.     

Combining spatial transition probabilities for stochastic simulation of categorical fields

Categorical spatial data, such as land use classes and socioeconomic statistics data, are important data sources in geographical information science (GIS). The investigation of spatial patterns implied in these data can benefit many aspects of GIS research, such as classification of spatial data, spatial data mining, and spatial uncertainty modeling. However, the discrete nature of categorical data limits the application of traditional kriging methods widely used in Gaussian random fields. In this article, we present a new probabilistic method for modeling the posterior probability of class occurrence at any target location in space-given known class labels at source data locations within a neighborhood around that prediction location. In the proposed method, transition probabilities rather than indicator covariances or variograms are used as measures of spatial structure and the conditional or posterior (multi-point) probability is approximated by a weighted combination of preposterior (two-point) transition probabilities, while accounting for spatial interdependencies often ignored by existing approaches. In addition, the connections of the proposed method with probabilistic graphical models (Bayesian networks) and weights of evidence method are also discussed. The advantages of this new proposed approach are analyzed and highlighted through a case study involving the generation of spatial patterns via sequential indicator simulation.     

Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea

The aim of this study is to analyze hydrothermal gold–silver mineral deposits potential in the Taebaeksan mineralized district, Korea, using an artificial neural network (ANN) and a geographic information system (GIS) environment. A spatial database considering 46 Au and Ag deposits, geophysical, geological, and geochemical data was constructed for the study area using the GIS. The geospatial factors were used with the ANN to analyze mineral potential. The Au and Ag mineral deposits were randomly divided into a training set (70%) to analyze mineral potential using ANN and a test set (30%) to validate predicted potential map. Four different training datasets determined from likelihood ratio and weight of evidence models were applied to analyze and validate the effect of training. Then, the mineral potential index (MPI) was calculated using the trained back-propagation weights, and mineral potential maps (MPMs) were constructed from GIS data for the four training cases. The MPMs were then validated by comparison with the test mineral occurrences. The validation results gave respective accuracies of 73.06, 73.52, 70.11, and 73.10% for the training cases. The comparison results of some training cases showed less sensitive to training data from likelihood ratio than weight of evidence. Overall, the training cases selected from 10% area with low and high index value of MPM_L and MPM_W gave higher accuracy (73.52 and 73.10%) for MPMs than those (73.06 and 70.11%, respectively) from known deposits and 10% area with low index value of MPI_L and MPI_W.     

Application of GIS Processing Techniques for Producing Mineral Prospectivity Maps—A Case Study: Mesothermal Au in the Swayze Greenstone Belt,Ontario, Canada

A Geographic Information System (GIS) is used to prepare and process digital geoscience data in a variety of ways for producing gold prospectivity maps of the Swayze greenstone belt, Ontario, Canada. Data used to produce these maps include geologic, geochemical, geophysical, and remotely sensed (Landsat). A number of modeling methods are used and are grouped into data-driven (weights of evidence, logistic regression) and knowledge-driven (index and Boolean overlay) methods. The weights of evidence (WofE) technique compares the spatial association of known gold prospects with various indicators (evidence maps) of gold mineralization, to derive a set of weights used to produce the final gold prospectivity map. Logistic regression derives statistical information from evidence maps over each known gold prospect and the coefficients derived from regression analysis are used to weight each evidence map. The gold prospectivity map produced from the index overlay process uses a weighting scheme that is derived from input by the geologist, whereas the Boolean method uses equally weighted binary evidence maps.The resultant gold prospectivity maps are somewhat different in this study as the data comprising the evidence maps were processed purposely differently for each modeling method. Several areas of high gold potential, some of which are coincident with known gold prospects, are evident on the gold prospectivity maps produced using all modeling methods. The majority of these occur in mafic rocks within high strain zones, which is typical of many Archean greenstone belts.     

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.     

Combining indicator patterns in weights of evidence modeling for resource evaluation

The weights of evidence model for combining indicator patterns in mineral resource evaluation is briefly explained with emphasis on the effect of undiscovered deposits on the estimation of the weights and posterior probabilities. A group of six statistical tests is proposed for analyzing the interaction of two or three indicator patterns with the point pattern for mineral deposits. A distinction is made between statistics that depend on choice of unit cell size and those that are approximately or completely independent of it. Finally, weights of evidence are compared to regression coefficients obtained by means of the logistic model.     

A Modified Weights-of-Evidence Method for Mineral Potential Prediction Based on Structural Equation Modeling

Structural equation modeling (SEM) was applied here to modify the ordinary weights-of-evidence (WofE) method for calculating posterior probability to improve conditional independence (CI) in the application of this method mineral potential prediction. The new method attempts to reduce the effect of CI by defining new binary patterns with an optimum combination of cutoff values of patterns. The solution is calculated through SEM, and the goodness of fit between evidence and mineral deposit occurrences is evaluated by a specified target function. The main difference between the new WofE and ordinary WofE is that evidence in the new method maintains a balance between the significance for mineral potential prediction and CI, rather than the significance for mineral prediction only as in ordinary WofE. A case study of prediction of potential for hydrothermal Au mineral deposits in Nova Scotia, Canada, is discussed here. The results indicate that the new method performs better than the ordinary WofE.

     

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.     

Logistic-Based Translation of Orogenic Gold Forming Processes into Mappable Exploration Criteria for Fuzzy Logic Mineral Exploration Targeting in the Kushaka Schist Belt,North-Central Nigeria

This paper applied a logistic-based fuzzy logic inference system to integrate critical factors that could control orogenic gold mineralization in part of the Kushaka schist belt, north-central Nigeria to develop a process-based mineral potential mapping (MPM) of the area. The critical factors from geophysical and geological dataset were weighted using logistic functions. The fuzzy logic inference system provides the capability to handle complex geological processes that culminated in orogenic gold mineralization as well as minimizing systemic uncertainties/fuzziness that often plague MPM. The results of this work show that granitic intrusions with fuzzy scores of 0.67–0.90 played a major role in generating high geothermal gradient in the area. Seventy percent of the existing gold mine sites in the area spatially coincide with metasedimentary rocks, having fuzzy scores of 0.7–0.9; this suggests metasedimentary rocks as being responsible for the production of gold fluid and ligands in the area. The evidence of hydrothermal activity, with fuzzy scores of 0.53 and 0.91, confirms the occurrence of mineralization associated with quartz veins and granite rocks. Lithological contacts and faults, having fuzzy scores of 0.60–0.80, presumably contribute to the localization of orogenic gold mineralization in the area. Emerging from the results, favorable zones for primary orogenic gold mineralization in the area occurred predominantly on granite gneiss and quartz veins. The mineral potential map was found consistent with the local geology, structural styles and hydrothermal alteration signatures in the area, and its validation using the existing locations of geochemical anomalies and prediction–area rate curve in the study area showed 75 and 72% agreement, respectively, thus confirming the reliability of the developed mineral potential map for resource management.

     

A Modified Weights-of-Evidence Method for Regional Mineral Resource Estimation

Weights-of-evidence (WofE) modeling and weighted logistic regression (WLR) are two methods of regional mineral resource estimation, which are closely related: For example, if all the map layers selected for further analysis are binary and conditionally independent of the mineral occurrences, expected WofE contrast parameters are equal to WLR coefficients except for the constant term that depends on unit area size. Although a good WofE strategy is supposed to achieve approximate conditional independence, a common problem is that the final estimated probabilities are biased. If there are N deposits in a study area and the sum of all estimated probabilities is written as S, then WofE generally results in S > N. The difference S − N can be tested for statistical significance. Although WLR yields S = N, WLR coefficients generally have relatively large variances. Recently, several methods have been developed to obtain WofE weights that either result in S = N, or become approximately unbiased. A method that has not been applied before consists of first performing WofE modeling and following this by WLR applied to the weights. This method results in modified weights with unbiased probabilities satisfying S = N. An additional advantage of this approach is that it automatically copes with missing data on some layers because weights of unit areas with missing data can be set equal to zero as is generally practiced in WofE applications. Some practical examples of application are provided.     

Application of Weights of Evidence Method for Assessment of Flowing Wells in the Greater Toronto Area,Canada

Flowing wells extracted from the Ministry of Ontario Environment and Energy (MOEE) water well data set from the Oak Ridges Moraine (ORM) area, Ontario, Canada, were treated as training point set to evaluate the potential distribution of artesian aquifers and their spatial associations with other geological and topological features in the study area. Evidential layers of geological and topographical features were constructed on the basis of the digital elevation model (DEM) and a geological map using GIS buffering functions in conjunction with weights of evidence method. It has been demonstrated that the locations of the flowing wells in the Oak Ridges Moraine area are associated spatially with the distances, (a) 500–5000 m from the oak ridges moraine deposits, (b) 500–4000 m from thick drift layer delineated on the drift thickness map created from water well data, and (c) 1500–2500 m from steep slope zones with slope above 8 degree calculated from a DEM. Applying a combination of these conditions can reduce the predicting target areas of having flowing wells by two thirds. Outcomes of this research are important both because the impact of the results on understanding of characteristics of aquifers and their relationships with other geological and topographical features and because it generates a probability map showing the potential location of artesian aquifers in the ORM area. In addition, the methodologies used in the paper will be applicable for modeling the distributions of other types of objects such as surface water bodies and low flow of streams in a watershed context in the study area.     

Classification of Soil Groups Using Weights-of-Evidence-Method and RBFLN-Neural Nets

Weights-of-Evidence (WofE) and Radial Basis Function Link Net (RBFLN) were applied to soil group mapping in eastern Finland. The data consisted of low altitude airborne geophysical measurements, Landsat 5 TM-satellite image, and digital elevation model (DEM) and slope information derived from it. Probability maps were constructed for each soil group one by one and combined into a prediction map of soil groups using maximum posterior probability (WofE) or pattern membership (RBFLN). Self-Organizing Map (SOM) and Sammon’s Mapping were applied for selecting the data sets for modeling and visualizing the data. The soil types belonging to each soil group used in the Arc-SDM modeling were defined by clusters revealed by the SOM and Sammon’s Mapping algorithms. The soil types with similar characters were collected in the same cluster. Numerical evaluation of the models’ performance was performed using the confusion matrix. The Ratio of Correct Classifications (RCC) for the best WofE model was 0.64 in the training area and 0.61 in the testing area. The RCC for the best RBFLN model was 0.62. Modeling of soil groups using Arc-SDM is time consuming because models need to be constructed for each soil group before combining them into a final prediction map. In this study a simple method was tested for combining the maps. In the future, more attention should be paid to combining the posterior probability models and also to selecting data sets used for modeling.     

基于SINMAP模型的区域滑坡危险性定量评估及模型验证

利用稳定态水文学理论与无限斜坡稳定性模型,构建分布式斜坡稳定性定量评估模型SINMAP,以坡体滑塌十分发育的陕西省略阳县为试验区,利用Grid DEM提取坡度、流向、地形湿度指数和有效汇水面积等流域地形水文数据,将GIS专题图、遥感数据等作为模型输入数据,获得地表斜坡稳定性分级专题图,实现滑坡危险性定量评估;将模型模拟结果与目前国内最具有权威性的中国县(市)地质灾害调查结果进行对比分析,发现两者在稳定性分级标准划分、滑坡点定性评价、滑坡危险性分区等方面都具有很好的相似性和可比性,说明模型的模拟结果能够客观反映研究区地表滑坡危险性,对可能出现的滑坡具有一定的预测精度。因此,该模型的研究有望为定量分析区域滑坡与环境因子的关系、区域滑坡预测等工作奠定基础。     

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.     

云南怒江流域泥石流敏感性空间分析

泥石流敏感性空间分析是通过评估诱发泥石流发生的因子,应用空间技术,进行泥石流发生的敏感性分析。本文探讨了GIS技术与敏感性分析的条件概率模型相结合的泥石流敏感性空间分析,并且阐述了GIS空间分析技术在泥石流敏感性制图中特有的优越性。研究区选择在遭受泥石流危害严重的云南怒江流域。用于泥石流敏感性分析评价的主要敏感因子包括地形坡度、岩土体类型、暴雨、河网密度、土地利用、地震动峰值加速度和人类活动。在对这些因子进行了敏感性空间分析的基础上,应用GIS的分析工具对敏感因子集成评价而产生了云南怒江流域泥石流敏感性评价图。泥石流敏感性评价图可以帮助规划者或工程师在土地发展规划中选择最佳建设场所,以减轻泥石流灾害的影响。