Geologically Constrained Fuzzy Mapping of Gold Mineralization Potential,Baguio District,Philippines

An application of the theory of fuzzy sets to the mapping of gold mineralization potential in the Baguio gold mining district of the Philippines is described. Proximity to geological features is translated into fuzzy membership functions based upon qualitative and quantitative knowledge of spatial associations between known gold occurrences and geological features in the area. Fuzzy sets of favorable distances to geological features and favorable lithologic formations are combined using fuzzy logic as the inference engine. The data capture, map operations, and spatial data analyses are carried out using a geographic information system. The fuzzy predictive maps delineate at least 68% of the known gold occurrences that are used to generate the model. The fuzzy predictive maps delineate at least 76% of the unknown gold occurrences that are not used to generate the model. The results are highly comparable with the results of previous stream-sediment geochemical survey in the area. The results demonstrate the usefulness of a geologically constrained fuzzy set approach to map mineral potential and to redirect surficial exploration work in the search for yet undiscovered gold mineralization in the mining district. The method described is applicable to other mining districts elsewhere.     

A Predictive Geospatial Exploration Model for Mississippi Valley Type Pb–Zn Mineralization in the Southeast Missouri Lead District

The Pb–Zn sulfide concentrations hosted by dolomitized Cambrian carbonates in Southeast Missouri are world-class Mississippi Valley Type (MVT) deposits. These deposits commonly are in sites where local Precambrian basement highs resulted in depositional pinchouts of the basal Cambrian sandstones that served as a regional aquifer for basinal fluid migration driven by late Paleozoic Ouachita deformation. Mineralization also appears to be spatially related to regional faults that probably served as local fluid conduits. Understanding spatial associations between sites of known mineralization and regional geology, geochemistry, and geophysics in Southeast Missouri will be a useful guide in future exploration efforts in this region and for similar geologic settings globally. The weights-of-evidence method is used to evaluate regional geology, geochemistry, and geophysical datasets and produce favorability maps for MVT deposits in Southeast Missouri. Host rock characteristics, regional structural controls, stream sediment geochemistry, and proximity to basement highs appear to be the most useful data for predicting the location of the major deposits. This work illustrates the potential utility of mineral potential modeling to prioritize areas for exploration and identify permissive areas for undiscovered MVT mineralization.

     

Mineral Potential Modelling for the Greater Nahanni Ecosystem Using GIS Based Analytical Methods

Mineral potential within the Greater Nahanni Ecosystem (GNE) was modelled in a Geographic Information System (GIS) for four different deposit types: (1) SEDEX (stratiform shale-hosted sedimentary exhalative Zn–Pb–Ag), (2) ‘Carbonate-Fault’ (carbonate-hosted zinc–lead–silver associated with major faults), (3) ‘Intrusion-Related’ (includes skarn, rare metals and gemstones) and (4) Carlin-Type gold as lode and/or derived placer deposits. This mineral potential modelling study integrates data collected during the Nahanni Mineral and Energy Resource Assessment (MERA) undertaken from 2003 to 2007. The results have contributed to the process of determining the geographic boundaries of the proposed expansion of the Nahanni National Park Reserve. Four mineral potential maps were produced (one for each deposit type) using a knowledge-driven approach. A weighting scheme based on integrated mineral deposit and regional geological knowledge was derived for the various evidence maps for each deposit model using expert opinion. The four potential maps were then combined into a final potential map using a maximum operator. Plots showing the efficiency of the models (mineral potential maps) for predicting the known occurrences of the four deposit types show that partial data sets provide reasonable predictions of the remaining known mineral prospects, occurrences and deposits. Hydrocarbon potential from Nahanni MERA 1 was added to the final potential map to ensure that both mineral and energy potential data were incorporated into the park configuration modelling.     

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.

     

Lithodiversity and Its Spatial Association with Metallic Mineral Sites,Great Basin of Nevada

Geographical information system (GIS) techniques were used to investigate the spatial association between metallic mineral sites and lithodiversity in Nevada. Mineral site data sets include various size and type subsets of about 5,500 metal-bearing occurrences and deposits. Lithodiversity was calculated by counting the number of unique geological map units within four sizes of square-shaped sample neighborhoods (2.5-by-2.5, 5-by-5, 10-by-10, and 20-by-20 km) on three different scales of geological maps (national, 1:2,500,000; state, 1:500,000; county, 1:250,000). The spatial association between mineral sites and lithodiversity was observed to increase with increasing lithodiversity. This relationship is consistent for (1) both basin-range and range-only regions, (2) four sizes of sample neighborhoods, (3) various mineral site subsets, (4) the three scales of geological maps, and (5) areas not covered by large-scale maps. A map scale of 1:500,000 and lithodiversity sampling neighborhood of 5-by-5 km was determined to best describe the association. Positive associations occurred for areas having >3 geological map units per neighborhood, with the strongest observed at approximately >7 units. Areas in Nevada with more than three geological map units per 5-by-5 km neighborhood contain more mineral sites than would be expected resulting from chance. High lithodiversity likely reflects the occurrence of complex structural, stratigraphic, and intrusive relationships that are thought to control, focus, localize, or expose mineralization. The application of lithodiversity measurements to areas that are not well explored may help delineate regional-scale exploration targets and provide GIS-supported mineral resource assessment and exploration activity another method that makes use of widely available geological map data.     

Structural Analysis for Gold Mineralization Using Remote Sensing and Geochemical Techniques in a GIS Environment: Island of Lesvos,Hellas

Exploration for epithermal Au has been active lately in the Aegean Sea of the eastern Mediterranean Basin, both in the islands of the Quaternary arc and in those of the back-arc region. The purpose of this study was the structural mapping and analysis for a preliminary investigation of possible epithermal gold mineralization, using remotely sensed data and techniques, structural and field data, and geochemical information, for a specific area on the Island of Lesvos. Therefore, Landsat-TM and SPOT-Pan satellite images and the Digital Elevation Model (DEM) of the study area were processed digitally using spatial filtering techniques for the enhancement and recognition of the geologically significant lineaments, as well as algebraic operations with band ratios and Principal Component Analysis (PCA), for the identification of alteration zones. Statistical rose diagrams and a SCHMIDT projection Stereo Net were generated from the lineament maps and the collected field data (dip and strike measurements of faults, joints, and veins), respectively. The derived lineament map and the band ratio images were manipulated in a GIS environment, in order to study the relation of the tectonic pattern to both the alteration zoning and the geomorphology of the volcanic field of the study area. Target areas of high interest for possible mineralization also were specified using geochemical techniques, such as X-Ray Diffraction (XRD) analysis, trace-element, and fluid-inclusion analysis. Finally, preliminary conclusions were derived about possible mineralization, the type (high or low sulfidation), and the extent of mineralization, by combining the structural information with geochemical information.     

Knowledge-Driven Prospectivity Mapping for Granite-Related Polymetallic Sn–F–(REE) mineralization,Bushveld Igneous Complex,South Africa

This paper presents mineral prospectivity mapping to identify potential new exploration ground for polymetallic Sn–F–REE mineralization associated with the Bushveld granites of the Bushveld Igneous Complex, South Africa. The Lebowa Granite Suite, commonly known as the Bushveld granites, is host to a continuum of polymetallic mineralization with a wide range of metal assemblages (Sn–Mo–W–Cu–Pb–Zn–As–Au–Ag–Fe–F–U–REE), ranging from a high-temperature to a low-temperature magmatic hydrothermal mineralizing environment. The prospectivity map was generated by fuzzy logic modeling and a selection of targeting criteria (or spatial proxies) based on a conceptual mineral system highlighting critical processes responsible for the formation of the polymetallic mineralization. The spatial proxies include proximity to differentiated granites (as heat and metal-rich fluid sources), Rb geochemical map (fluid-focusing mechanism such as fractionation process), principal component maps (PC 4 Y–Th and PC 14 Sn–W, fluid pathways for both high- and low-temperature mineralization) and proximity to roof rocks (traps for fluids). Logarithmic functions were used to rescale rasterized evidential maps into continuous fuzzy membership scores in a range of [0, 1]. The evidential maps were combined in two-staged integration matrix using fuzzy AND, OR and gamma operators to produce the granite-related polymetallic Sn–F–(REE) prospectivity map. The conceptual mineral system model and corresponding prospectivity model developed in this study yielded an encouraging result by delineating the known mineral deposits and occurrences of Sn–F–(REE) mineralization that were not used to assign weights to the evidential maps. The prospectivity model predicted, on average, 77% of the known mineral occurrences in the BIC (i.e., 56 of 73 Sn occurrences, 12 of 15 F occurrences and 6 of 8 REE occurrences). Based on this validation, 13 new targets were outlined in this study.     

A Comparative Analysis of Weights of Evidence,Evidential Belief Functions,and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation

Large amounts of digital data must be analyzed and integrated to generate mineral potential maps, which can be used for exploration targeting. The quality of the mineral potential maps is dependent on the quality of the data used as inputs, with higher quality inputs producing higher quality outputs. In mineral exploration, particularly in regions with little to no exploration history, datasets are often incomplete at the scale of investigation with data missing due to incomplete mapping or the unavailability of data over certain areas. It is not always clear that datasets are incomplete, and this study examines how mineral potential mapping results may differ in this context. Different methods of mineral potential mapping provide different ways of dealing with analyzing and integrating incomplete data. This study examines the weights of evidence (WofE), evidential belief function and fuzzy logic methods of mineral potential mapping using incomplete data from the Carajás mineral province, Brazil to target for orogenic gold mineralization. Results demonstrate that WofE is the best one able to predict the location of known mineralization within the study area when either complete or unacknowledged incomplete data are used. It is suggested that this is due to the use of Bayes’ rule, which can account for “missing data.” The results indicate the effectiveness of WofE for mineral potential mapping with incomplete data.     

柴达木盆地中部盐湖环境遥感初步解译

柴达木盆地是我国主要的盐湖分布区 ,通过美国陆地卫星TM标准假彩色影像图 ,对该盆地中部地的盐湖环境、沙漠分布与变化、地质构造、1 989年洪水的影响范围等自然地质地理特征进行了初步的研究     

Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)

An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area.     

Spatial Modeling for Base-Metal Mineral Exploration Through Integration of Geological Data Sets

This study involves the integration of information interpreted from data sets such as LandsatTM, Airborne magnetic, geochemical, geological, and ground-based data of Rajpura—Dariba,Rajasthan, India through GIS with the help of (1) Bayesian statistics based on the weights ofevidence method and (2) a fuzzy logic algorithm to derive spatial models to target potentialbase-metal mineralized areas for future exploration. Of the 24 layers considered, five layers(graphite mica schist (GMS), calc-silicate marble (CALC), NE-SW lineament 0–2000 mcorridor (L4-NESW), Cu 200–250 ppm, and Pb 200–250 ppm) have been identified from theBayesian approach on the basis of contrast. Thus, unique conditions were formed based onthe presence and absence of these five map patterns, which are converted to estimate posteriorprobabilities. The final map, based on the same data used to determine the relationships, showsfour classes of potential zones of sulfide mineralization on the basis of posterior probability.In the fuzzy set approach, membership functions of the layers such as CALC, GMS, NE-SWlineament corridor maps, Pb, and Cu geochemical maps have been integrated to obtain thefinal potential map showing four classes of favorability index.     

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.     

Natural Gas Hydrates in the Offshore Beaufort–Mackenzie Basin—Study of a Feasible Energy Source II

In the offshore part of Beaufort–Mackenzie Basin depth of methane hydrate stability reaches more than 1.5 km. However, there are areas in the western part of the basin where there are no conditions of methane hydrate stability. Construction of the first contour maps displaying thickness of hydrate stability zones as well as hydrate stability zone thicknesses below permafrost in the offshore area, shows that these zones can reach 1200 m and 900 m, respectively. Depth to the base of ice-bearing relict permafrost under the sea (depth of the –1°C isotherm-ice-bearing permafrost base) and regional variations of geothermal gradient are the main controlling factors. Hydrostatic pressures in the upper 1500 m are the rule. History of methane hydrate stability zone is related mainly to the history of permafrost and it reached maximum depth in early Holocene. More recently, the permafrost and hydrate zone is diminishing because of sea transgression. Reevaluation of the location of possible gas hydrate occurrences is done from the analysis of well logs and other indicators in conjunction with knowledge of the hydrate stability zone. In the offshore Beaufort–Mackenzie Basin, methane hydrate occurs in 21 wells. Nine of these locations coincides with underlying conventional hydrocarbon occurrences. Previous analyses place some of the hydrate occurrences at greater depths than proposed for the methane hydrate-stability zone described in this study. Interpretation of geological cross sections and maps of geological sequences reveals that hydrates are occurring in the Iperk–Kugmallit sequence. Hydrate–gas contact zones, however, are possible in numerous situations. As there are no significant geological seals in the deeper part of the offshore basin (all hydrates are within Iperk), it is suggested that overlying permafrost and hydrate stability zone acted as the only trap for upward migrating gas during the last tens of thousand of years (i.e., Sangamonian to Holocene).     

Study of the Natural Gas Hydrate “Trap Zone” and the Methane Hydrate Potential in the Sverdrup Basin,Canada

The methane hydrate stability zone beneath Sverdrup Basin has developed to a depth of 2 km underneath the Canadian Arctic Islands and 1 km below sea level under the deepest part of the inter-island sea channels. It is not, however, a continuous zone. Methane hydrates are detected in this zone, but the gas hydrate/free gas contact occurs rarely. Interpretation of well logs indicate that methane hydrate occurs within the methane stability zone in 57 of 150 analyzed wells. Fourteen wells show the methane hydrate/free gas contact. Analysis of the distribution of methane hydrate and hydrate/gas contact occurrences with respect to the present methane hydrate stability zone indicate that, in most instances, the detected methane hydrate occurs well above the base of methane hydrate stability. This relationship suggests that these methane hydrates were formed in shallower strata than expected with respect to the present hydrate stability zone from methane gases which migrated upward into hydrate trap zones. Presently, only a small proportion of gas hydrate occurrences occur in close proximity to the base of predicted methane hydrate stability. The association of the majority of detected hydrates with deeply buried hydrocarbon discoveries, mostly conventional natural gas accumulations, or mapped seismic closures, some of which are dry, located in structures in western and central Sverdrup Basin, indicate the concurring relationship of hydrate occurrence with areas of high heat flow. Either present-day or paleo-high heat flows are relevant. Twenty-three hydrate occurrences coincide directly with underlying conventional hydrocarbon accumulations. Other gas hydrate occurrences are associated with structures filled with water with evidence of precursor hydrocarbons that were lost because of upward leakage.     

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.     

Integration of Structural,Gravity, and Magnetic Data Using the Weights of Evidence Method as a Tool for Kimberlite Exploration in the Buffalo Head Hills,Northern Central Alberta,Canada

In the Buffalo Head Hills area a weights of evidence statistical approach was used to determine the spatial relationship of NNE-, NE-, NW-, and ENE-trending lineaments to known kimberlite locations. This method outlined different degrees of spatial correlation between and lineaments, with higher correlations defined for the NNE, NE, and ENE lineament data sets. A weights of evidence model then was constructed using the structural lineament maps, the Buffalo High and Buffalo Utikuma terrane boundary, Bouguer gravity data, and magnetic characteristics of the Buffalo High and Buffalo Utikuma terranes. The model reveals maximum favorability for kimberlite exploration along the Buffalo High and Utikuma terrane boundary in correspondence with NNE-trending lineaments and their intersections with NE and ENE lineaments. The relationship of the kimberlite occurrences along the Buffalo High-Buffalo Utikuma terrane boundary and structural lineaments seems to favor an hypothesis of kimberlite emplacement through a major zone of weakness in the basement, here characterized by the boundary between the Buffalo High and Buffalo Utikumaterranes.     

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.     

Multicriteria Geologic Data Analysis for Mineral Favorability Mapping: Application to a Metal Sulphide Mineralized Area,Ribeira Valley Metallogenic Province,Brazil

GIS techniques have been used in the evaluation of favorability for base-metal mineralization in an area comprising the Cerro Azul and Apiaí quadrangles (SG.22-X-B-IV and V, scale 1:100.000), Ribeira Valley, São Paulo and Paraná States, Brazil. Methods have been employed for selection and weighting of prospective variables when applying GIS techniques to a digital database consisting of geological, geochemical and airborne geophysics, and mineral occurrence information. The exploration variable selection and analysis were based on two mineralization models: (1) Panelas type, vein-type carbonate hosted, and (2) Perau type, sedimentary-exhalative. The overlay was performed by weighted linear combination (WLC) and order weighted average (OWA) methods. Both methods proved suitable for the study area, yielding similar results. The ordered weighted averaging analysis provided the best results, with favorability maps showing a large number of classes occupying relatively minor areas. In comparison, the weighted linear combination analysis produced more coherent results but without details for minor areas. The prospective parameters obtained are considered suitable for both Perau and Panelas types. Both methods are inexpensive, and are suitable for selection of prospective areas during geological surveys in areas similar to the studied one.     

Cause-Effect Analysis in Assessment of Mineral Resources

Cause-effect analyses is a deterministic methodology intended for processing qualitative (e.g. texts, conventional maps) and mixed, qualitative and quantitative, data. The main idea, employed in cause-effect analysis, is the plurality and interaction of causes. This idea is described by mathematical logic formulae that can be converted into a single Boolean equation. The latter represents a mathematical model of a general shape of cause-effect relations for the study problem. In particular, such a model can express relations between some property of the mineralization and features of other geological phenomena. By processing data, logical dependencies satisfying to the theoretical model are determined in a data file. These dependences, expressed by Boolean function formulae, describe cause-effect relations for a case study, and they are used for predicting. Software realizing the cause-effect analysis is an expert system with artificial intelligence capabilities. There are two methods of using the cause-effect analysis in assessment of mineral resources. The first method consists in detecting the regularity in locations of known mineral deposits and occurrences with the following using the regularity formula for generation of predictive maps. The second method is the evaluation of individual mineral occurrences by obtained Boolean formulae expressing cause-effect relations between deposit sizes and geological environment of deposits. Both methods are illustrated by case studies of predicting gold-bearing deposits of Middle Asia in the former USSR.     

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.