The representation of geoscience information for data integration

In mineral exploration, resource assessment, or natural hazard assessment, many layers of geoscience maps such as lithology, structure, geophysics, geochemistry, hydrology, slope stability, mineral deposits, and preprocessed remotely sensed data can be used as evidence to delineate potential areas for further investigation. Today's PC-based data base management systems, statistical packages, spreadsheets, image processing systems, and geographical information systems provide almost unlimited capabilities of manipulating data. Generally such manipulations make a strategic separation of spatial and nonspatial attributes, which are conveniently linked in relational data bases. The first step in integration procedures usually consists of studying the individual charateristics of map features and interrelationships, and then representing them in numerical form (statistics) for finding the areas of high potential (or impact).Data representation is a transformation of our experience of the real world into a computational domain. As such, it must comply with models and rules to provide us with useful information. Quantitative representation of spatially distributed map patterns or phenomena plays a pivotal role in integration because it also determines the types of combination rules applied to them.Three representation methods—probability measures, Dempster-Shafer belief functions, and membership functions in fuzzy sets—and their corresponding estimation procedures are presented here with analyses of the implications and of the assumptions that are required in each approach to thematic mapping. Difficulties associated with the construction of probability measures, belief functions, and membership functions are also discussed; alternative procedures to overcome these difficulties are proposed. These proposed techniques are illustrated by using a simple, artificially constructed data set.     

Map Scale Effects on Estimating the Number of Undiscovered Mineral Deposits

Estimates of numbers of undiscovered mineral deposits, fundamental to assessing mineral resources, are affected by map scale. Where consistently defined deposits of a particular type are estimated, spatial and frequency distributions of deposits are linked in that some frequency distributions can be generated by processes randomly in space whereas others are generated by processes suggesting clustering in space. Possible spatial distributions of mineral deposits and their related frequency distributions are affected by map scale and associated inclusions of non-permissive or covered geological settings. More generalized map scales are more likely to cause inclusion of geologic settings that are not really permissive for the deposit type, or that include unreported cover over permissive areas, resulting in the appearance of deposit clustering. Thus, overly generalized map scales can cause deposits to appear clustered. We propose a model that captures the effects of map scale and the related inclusion of non-permissive geologic settings on numbers of deposits estimates, the zero-inflated Poisson distribution. Effects of map scale as represented by the zero-inflated Poisson distribution suggest that the appearance of deposit clustering should diminish as mapping becomes more detailed because the number of inflated zeros would decrease with more detailed maps. Based on observed worldwide relationships between map scale and areas permissive for deposit types, mapping at a scale with twice the detail should cut permissive area size of a porphyry copper tract to 29% and a volcanic-hosted massive sulfide tract to 50% of their original sizes. Thus some direct benefits of mapping an area at a more detailed scale are indicated by significant reductions in areas permissive for deposit types, increased deposit density and, as a consequence, reduced uncertainty in the estimate of number of undiscovered deposits. Exploration enterprises benefit from reduced areas requiring detailed and expensive exploration, and land-use planners benefit from reduced areas of concern.     

Integration of Sparse Geologic Information in Gold Targeting Using Logistic Regression Analysis in the Hutti–Maski Schist Belt,Raichur, Karnataka,India—A Case Study

Logistic regression has been used in the study to integrate indicator patterns for estimation of the probability of occurrence of gold deposits in a part of the auriferous Archaean Hutti–Maski schist belt. Data used consist of categorical and continuous variables obtained from a coded lineament map and geochemical anomaly maps of the pathfinder elements of gold in soil and groundwater. Main effects and interactions of the variables studied were used in formulating the logistic regression model. Regression models using lineament-proximity data, combined with soil and groundwater geochemical anomalies were tested on parts of the schist belt with data not used in estimation of model parameters. Predicted probabilities greater than 0.9 identified known deposit locations in the area.     

GIS-based Mineral Prospectivity Mapping of Seafloor Massive Sulfide on Ultraslow-spreading Ridges: A Case Study of Southwest Indian Ridge 48.7°–50.5° E

With the depletion of mineral resources on land, seafloor massive sulfide deposits have the potential to become as important for exploration, development and mining as those on land. However, it is difficult to investigate the ocean environment where seafloor massive sulfide deposits are located. Thus, improving prospecting efficiency by reducing the exploration search space through mineral prospectivity mapping (MPM) is desirable. MPM has been used in the exploration for seafloor deposits on regional scales, e.g., the Mid-Atlantic Ridge and Arctic Ridge. However, studies of MPM on ultraslow-spreading ridges on segment scales to aid exploration for seafloor massive sulfide have not been carried out to date. Here, data of water depth, geology and hydrothermal plume anomalies were analyzed and the weights-of-evidence method was used to study the metallogenic regularity and to predict the potential area for seafloor massive sulfide exploration in 48.7°–50.5° E segments on the ultraslow spreading Southwest Indian Ridge. Based on spatial analysis, 11 predictive maps were selected to establish a mineral potential model. Weight values indicate that the location of seafloor massive sulfide deposits is correlated mainly with mode-E faults and oceanic crust thickness in the study area, which correspond with documented ore-controlling factors on other studied ultraslow-spreading ridges. In addition, the detachment fault and ridge axis, which reflect the deep hydrothermal circulation channel and magmatic activities, also play an important role. Based on the posterior probability values, 3 level A, 2 level B and 2 level C areas were identified as targets for further study. The MPM results were helpful for narrowing the search space and have implications for investigating and evaluating seafloor massive sulfide resources in the study area and on other ultraslow-spreading ridges.

     

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.     

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.     

Multi-scale Quantitative Risk Analysis of Seabed Minerals: Principles and Application to Seafloor Massive Sulfide Prospects

The potential for mining hydrothermal mineral deposits on the seafloor, such as seafloor massive sulfides, has become technically possible, and some companies (currently not many) are considering their exploration and development. Yet, no present methodology has been designed to quantify the ore potential and assess the risks relative to prospectivity at prospect and regional scales. Multi-scale exploration techniques, similar to those of the play analysis that are used in the oil and gas industry, can help to fulfill this task by identifying the characteristics of geologic environments indicative of ore-forming processes. Such characteristics can represent a combination of, e.g., heat source, pathway, trap and reservoir that all dictate how and where ore components are mobilized from source to deposition. In this study, the understanding of these key elements is developed as a mineral system, which serves as a guide for mapping the risk of the presence or absence of ore-forming processes within the region of interest (the permissive tract). The risk analysis is carried out using geoscience data, and it is paired with quantitative resource estimation analysis to estimate the in-place mineral potential. Resource estimates are simulated stochastically with the help of available data (bathymetric features in this study), conventional grade–tonnage models and Monte Carlo simulation techniques. In this paper, the workflow for a multi-scale quantitative risk analysis, from the definition to the evaluation of a permissive tract and related prospect(s), is described with the help of multi-beam data of a known hydrothermal vent site.

     

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.     

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.     

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.

     

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.     

Landslide Representation Strategies in Susceptibility Studies using Weights-of-Evidence Modeling Technique

This paper is focused primarily on how to represent landslide scarp areas, how to analyze results achieved by the application of specific strategies of representation and how to compare the outcomes derived by different tests, within a general framework related to landslide susceptibility assessment. These topics are analyzed taking into account the scale of data survey (1:10,000) and the role of a landslide susceptibility map into projects targeted toward the definition of prediction, prevention, and mitigation measures, in a wider context of civil protection planning. These aims are achieved by using ArcSDM (Arc Spatial Data Modeler), a software extension to ArcView GIS useful for developing spatial prediction models using regional datasets. This extension requires a representation by points of the investigated problems (landslide susceptibility, aquifer vulnerability, detection of mineral deposits, identification of natural habitats of animals, and plants, etc.). Maps of spatial evidence from regional geological and geomorphological datasets were used to generate maps showing susceptibility to slope failures in two different study areas, located in the northern Apennines and in the central Alps (Italy), respectively. The final susceptibility maps for both study areas were derived by the application of the weights-of-evidence (WofE) modeling technique. By this method a series of subjective decisions were required, strongly dependent on an understanding of the natural processes under study, supported by statistical analysis of the spatial associations between known landslides and evidential themes. Except for maps of attitude, permeability, and structure, that were not available for both study areas, the other data were the same and comprised geological, land use, slope, and internal relief maps. The paper illustrates how different representations of scarp areas by points (in terms of different number of points) did not greatly influence the final response map, considering the scale of this work. On the contrary, some differences were observed in the capability of the model to describe the relations between predictor variables and landslides. In effect, a representation of the scarp areas using one point every 50 m led to a more efficient model able to better define relationships of this type. It avoided both problems of redundancy of information, deriving by the use of too many points, and problems related to a random positioning of the centroid. Moreover, it permitted to minimize the uncertainty related with identification and mapping of landslides.     

Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)

The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e., <20) mineral occurrences and (b) if RF modeling can handle predictor variables with missing values. We found that RF modeling outperforms evidential belief (EB) modeling of prospectivity for hydrothermal Au–Cu deposits in Catanduanes Island, where 17 hydrothermal Au–Cu prospects are known to exist. Moreover, just like EB modeling, RF modeling allows analysis of the spatial relationships between known prospects and individual layers of predictor data. Furthermore, RF modeling can handle missing values in predictor data through an RF-based imputation technique whereas in EB modeling, missing values are simply represented by maximum uncertainty. Therefore, the RF algorithm is a potentially useful method for data-driven predictive modeling of mineral prospectivity in regions with few (i.e., <20) occurrences of mineral deposits of the type sought. However, further testing of the method in other regions with few mineral occurrences is warranted to fully determine its usefulness in data-driven predictive modeling of mineral prospectivity.     

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.     

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.     

Quantitative estimates of the geology of large regions and their application to mineral-resource assessment

The unit regional value of the mineral resources of a large region may be estimated by accumulating past production records and prorating them over the area of the region. The geological characteristics of a large region is a prime conditioning variable for this purpose. To be useful, however, the geology of a large region must be represented in a standardized form. The “geology,” as here measured, refers to a standardized set of rock types common to the legends in geological maps. By using standardized procedures, the legends of 413 geologic maps at 292 different scales that cover the Earth’s land surface were transformed into a set of 65 three-digit numbers. The set of numbers called the time-petrographic index is associated with the contemporaneous tectonic environments that led to the formation of the rocks and their associated mineral deposits. Application of the time-petrographic index to geologic maps leads to more precise estimates of the mineral-resource values of a large region. Deceased, June 2, 1992     

Significance of the clay mineral distribution in fluvial sediments of the Neogene to Recent Himalayan Foreland Basin (west‐central Nepal)

Clay mineral assemblages of the Neogene Himalayan foreland basin are studied to decipher their significance with respect to tectonic and climate processes. Fluvial deposits of the Siwalik Group (west‐central Nepal), and sediment of the Ganga River drainage system were analysed for clay mineralogy. The observed clay mineral assemblages are mainly composed of illite (dominant), chlorite, smectite and kaolinite. Illite and chlorite are chiefly of detrital origin, derived from Himalayan sources. Kaolinite and smectite are authigenic, and mainly developed within pore space and as coating of detrital particles. With increasing burial, diagenetic processes affected the original clay mineral signature. Illitisation of smectite and kaolinite occurred below 2500 and 3500 m depth, respectively. Therefore, illite in the lower parts of the Siwalik Group consists of a mixture of inherited illite and illitised smectite and kaolinite, as suggested by illite crystallinity. Detrital grains that make up the framework of the Siwalik Group sandstones mainly consist of quartz, feldspar and lithic fragments, which are principally of sedimentary and metamorphic origin. Lithoclast content increases over time at the expense of quartz and K‐feldspar in response to uplift and erosion of the Lesser Himalaya Series since about 11–10 Ma. Despite mainly felsic source rocks, dominantly physical erosion processes in the Himalayan belt, and high‐energy fluvial depositional systems, smectite is abundant in the <7 Ma Siwalik Group deposits. Analyses of the Siwalik deposits and comparison with the clay mineralogy of the modern drainage system suggest that smectite preferentially formed in floodplains and intermontane valleys during early diagenesis because of downward percolating fluids rich in cations from weathering and soil development. In general, increasing seasonality and aridity linked to variability of the Asian monsoon from about 8 Ma enhanced clay mineral formation and development of authigenic smectite in paleo‐plains on the southern side of the Himalaya.     

Potential Use of Remote Sensing Techniques for Exploration of Iron Deposits in Western Sahara and Southwest of Algeria

At present, Western Sahara is politically one of the most sensitive areas of the World. Its economic development could be achieved through the exploitation of mineral resources that can be found in the almost unexplored area administrated by the Saharawi Arab Democratic Republic. In this paper, we describe applications of known and cost-effective remote sensing techniques to detect and map areas containing mineral deposits, through the enhancement of Landsat ETM+ imageries. Several image processing techniques (false color composite, band ratioing, and principal component analysis) were used to highlight the presence of iron deposits. Two test areas were selected, one in Western Sahara and another one in Algeria. The occurrence of iron deposits in these test areas was assured using literature data for the Algerian test site and through a field campaign for the Western Sahara. There is good agreement between the ground truth data and the results obtained by the enhancements of the satellite images. Landsat images can be downloaded free of charge and their enhancements does not need expensive hardware or software tools. Therefore this technology could be transferred to the Saharawi technicians, enabling them to explore and manage the mineral resources of their own country independently.     

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.