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The application of deep learning algorithms in mineral prospectivity mapping (MPM) is a hot topic in mineral exploration. However, few studies have focused on recurrent neural networks (RNNs) in terms of integrating different evidential layers to map mineral potential. In this study, a gated recurrent unit (GRU) model was employed for MPM using a case study on the Baguio District of Philippines. To generate sufficient training samples for GRU, data augmentation with geological constraints was employed. To explore the influence of different orders of evidence layers, as inputs of RNN, a total permutation of the evidence layers was built. Meanwhile, a nonlinear controlling function was used to capture the spatial relationships between known mineral deposits and geological controlling factors and assign the value for each pixel of evidence layers. The obtained results demonstrated the excellent performance of GRU in MPM. The delineated high-anomaly areas show close spatial relationships with known mineral deposits and therefore can provide significant clues for the next round of mineral exploration in the study area.

The South African gold mining sector remains a significant contributor to the country’s economy. Facing several challenges that hinder the realisation of South Africa’s full mineral potential, the sector’s sustainability and profitability can be enhanced through implementing operational improvement measures. Mobile cooling units (MCUs) were identified as a potential focus area for operational improvement. MCUs are used as tertiary or in-stope cooling in hot underground workings. In this paper, a method was presented to characterise the performance of existing MCUs based on three key performance indicators (KPIs), namely, the wet-bulb temperature ratio (WTR), efficiency and position. Optimisation strategies were then elected and implemented based on these KPIs. The implementation of this method in a South African gold mine attained a reduction in pumped water volumes, reduced operating costs through electricity cost savings and improvements in underground ventilation air temperatures.

This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on analyzing the spatial associations between geological prospecting big data (GPBD) and locations of known mineralization. Geodata science reveals the inter-correlations between GPBD and mineralization, converts GPBD into mappable criteria, and combines multiple mappable criteria into a mineral potential map. A workflow of the GSMPM is proposed and compared with the traditional workflow of mineral prospectivity mapping. More specifically, each component in such a workflow is explained in detail to demonstrate how geodata science serves mineral prospectivity mapping by deriving geoinformation from geoscience data, generating geo-knowledge from geoinformation, and allowing spatial decision-making by integrating geoinformation and geo-knowledge on the formation of mineral deposits. This review also presents several research directions for GSMPM in the future.

With the increasing use of machine learning for big data analytics, several methods have been implemented for the purpose of exploration targeting using mineral potential mapping in a GIS environment. Random forests (RF) have been successfully applied to data-driven mineral potential mapping using relatively small numbers of input maps that have typically been pre-classified by a geologist familiar with the mineral system being targeted. However, it is useful to understand how well RF perform for mineral potential mapping when a large number of multi-class categorical or non-thresholded numeric input maps are used in the classification or when weighted or ranked training data are used. Four different implementations of RF are presented to examine how the results vary depending on the degree of intervention from an expert in the modeling process. A case study has been devised using data from the eastern Lachlan Orogen in New South Wales (Australia) for the purposes of targeting porphyry Cu–Au mineralization related to the Macquarie Arc. The results demonstrate that the use of a large number of multi-class categorical or non-thresholded numeric predictive input maps results in a poor mineral potential map outcome. An expert review to determine reclassifications or thresholds that produce geologically meaningful maps as proxies for the mineral system being targeted results in more effective RF-based mineral potential maps being produced. Weighting or ranking the deposits used as training data produces more narrowly defined prospective areas that may assist with targeting tier-one economic deposits. Comparison of the RF results to a standard weights of evidence analysis highlighted some significant differences in which predictive maps should be considered important for modeling, and in the extent of prospective area delineated from each output mineral potential map.

This paper reviews governance challenges and opportunities in six Central American and Caribbean countries (Belize, Dominican Republic, Guatemala, Honduras, Nicaragua and Panama) as they develop new extractive projects. Despite important differences, governments in these countries share the need to improve their capacity to manage and control growing mining operations and potential revenues. In this paper we argue that mineral extraction faces challenges that go beyond traditional prescriptions to strengthen governance, such as environmental impacts and the cost born by local communities in terms of loss of land, water sources and culture; the right to free, informed and fair consultation; and polarization among the different stakeholders. Yet, we claim that transparency offers a starting point for governments to build trust and to demonstrate that they are prepared to address concerns surrounding mining operations, managing potential mineral windfalls and taking into consideration local community interests.

The first step in systematic exploration studies is mineral potential mapping, which involves classification of the study area to favorable and unfavorable parts. Support vector machines (SVM) are designed for supervised classification based on statistical learning theory. This method named support vector classification (SVC). This paper describes SVC model, which combine exploration data in the regional-scale for copper potential mapping in Kerman copper bearing belt in south of Iran. Data layers or evidential maps were in six datasets namely lithology, tectonic, airborne geophysics, ferric alteration, hydroxide alteration and geochemistry. The SVC modeling result selected 2220 pixels as favorable zones, approximately 25 percent of the study area. Besides, 66 out of 86 copper indices, approximately 78.6% of all, were located in favorable zones. Other main goal of this study was to determine how each input affects favorable output. For this purpose, the histogram of each normalized input data to its favorable output was drawn. The histograms of each input dataset for favorable output showed that each information layer had a certain pattern. These patterns of SVC results could be considered as regional copper exploration characteristics.

The distribution patterns of trace elements are a very useful tool for the prediction of mineral deposits occurrence and possible future exploitation. Machine learning techniques were used for the computation of adequate models in trace elements’ prediction. The main subject of this research is to define an adequate model to predict the amounts of Sn and W in the abandoned mine area of Lardosa (Central Portugal). The geochemical composition of 333 stream sediment samples collected in the study area was used. Total concentrations of As, B, Be, Cd, Co, Cr, Cu, Fe, Ni, P, Sn, U, V, W, Y, and Zn were used to define the best prediction model. Different machine learning techniques were tested: decision trees (CART), multilayer perceptron (MLP) and support vector machines (SVM) For regression and clustering, CART, MLP and SVM for the classification problem. These algorithms were used with six different inputs – N1 to N6 - to pick out the best-performing model. The results indicate that CART achieves the best predictions for Sn and W. In the regression process, correlation coefficients of 0.67 for Sn (with Input N1) and 0.70 28 for W (with Input N3) were obtained. Regarding the classification problem, an error rate of 0.10 for both Sn (Input N1) and W (Input N2) was reached. The classification process is the best methodology to use in the prediction of Sn and W using the trace element concentration of stream sediments from Lardosa area., Thanks are due to Prof. M.R. Machado Leite for the use of data on stream sediments from Instituto Geológico e Mineiro, S. Mamede de Infesta (Portugal). C. Iglesias acknowledges the Spanish Ministry of Education, Culture and Sports for FPU 12/02283 grant. This research was carried out under the CERENA/FEUP (Natural resources and Environment Center), Portugal. The author acknowledges the funding provided by the Institute of Earth Sciences (ICT), under contracts UID/ GEO/04683/2013 with FCT (the Portuguese Science and Technology Foundation) and COMPETE POCI-01-0145-FEDER-007690.

Ideas of resource becoming have become prominent across scholarship in resource geography over the past two decades. In this article, I link processes of becoming with those of place. Drawing on research focused on copper exploration and the junior mining sector, I describe the way resource potential is articulated through three distinct practices of placing: the placing of resource prospects within narratives of copper deficits; the placing of resource prospects in space; and the placing of resource prospects in juxtaposition to other projects. These promotional, taken-for-granted practices within the junior mining industry not only underscore the way resource potential is tied to spatial and temporal narratives, but illustrate how the locus of resource potential is found in areas beyond the underground.

A restricted Boltzmann machine can be trained to encode and reconstruct training samples from a training sample population with an unknown complex probability distribution. Small probability samples can be differentiated from the training sample population due to their comparatively larger encoded and reconstructed errors. In mineral potential mapping, mineral potential areas usually take up only a small proportion of the whole mineral exploration region and have specific evidence map patterns. Assume that the whole mineral exploration region is divided into uniform cells and a restricted Boltzmann machine is then trained on all the cells. As a small proportion of the total training cells, mineral potential cells have much larger encoded and reconstructed errors compared to non-mineral potential cells. Based on the encoded and reconstructed errors of the training cells, ASC and ASE are thus defined as two mineral potential indicators for mineral potential mapping with the trained restricted Boltzmann machine. The Altay district in northern Xinjiang in China, which is linked with a complex geological setting, is chosen as a case study area. Restricted Boltzmann machines with 12 visible units and differing hidden units are constructed and trained on 9582 training cells in the study area. The ASC, ASE, posterior probability, and deposit-bearing probability are used to predict the mineral potential of each grid cell in the study area. The AUC (area under the curve), a measure of aggregated classification performance in machine learning, is applied to measure the mineral potential mapping performance of these four mineral potential indicators. The results show that the four mineral potential indicators perform similarly well. Therefore, a restricted Boltzmann machine can be trained to map mineral potentials, and the ASC and ASE are two feasible indicators for the mineral potential mapping with the trained restricted Boltzmann machine.

The modelling or prediction of complex geospatial phenomena (like formation of geo-hazards) is one of the most important tasks for geoscientists. But in practice it faces various difficulties, caused mainly by the complexity of relationships between the phenomena itself and the controlling parameters, as well by limitations of our knowledge about the nature of physical/ mathematical relationships and by restrictions regarding accuracy and availability of data. In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling approach compared to the exact mathematical modelling. In the past, the application of ANN technologies in geosciences was primarily limited due to difficulties to integrate it into geo-data processing algorithms. In consideration of this background, the software advangeo® was developed to provide a normal GIS user with a powerful tool to use ANNs for prediction mapping and data preparation within his standard ESRI ArcGIS environment. In many case studies, such as land use planning, geo-hazards analysis and prevention, mineral potential mapping, agriculture & forestry advangeo® has shown its capabilities and strengths. The approach is able to add considerable value to existing data.

Rajasthan is one of the main mineral potential State of India and during last 30 years it has witnessed enormously expansion of mining industries but mining of most of the minor minerals coupled with changing climate has posed serious problems to the environmental fabric in the State, apart from base metal beneficiation plants. Groundwater is also being polluted day by day by effluents generated from mineral wastes and beneficiation processes in the vicinity of mining sites such as: Khetri & Zawar etc. Mining industry has deteriorated quality of groundwater resources in the State of Rajasthan and these industries are becoming centers of pollution sources which need timely actions at government level so that natural resources such as groundwater can be protected.

Selection of potential areas for mineral exploration is a complex process and needs many diverse criteria. Combining analytic hierarchy process (AHP) modeling with geographic information system (GIS) provides an effective means for studies of mineral potential mapping evaluation. Fuzzy AHP is an extension of conventional AHP and by using fuzzy theory is obtained the advantage rather AHP method. In this paper to provide, potential mapping for Cu porphyry mineralization used fuzzy AHP and GIS in the Ahar–Arasbaran areas, several criteria, such as geology, geochemical and geophysical data, alteration, and faults were used. Each criterion was evaluated with the aid of fuzzy AHP and mapped by GIS. The method allowed a mixture of quantitative and qualitative information with group decision. The results and its validation demonstrate the acceptable outcomes for copper porphyry exploration.

Using the analytic hierarchy process (AHP) method for multi-index evaluation has special advantages, while the use of geographic information systems (GIS) is suitable for spatial analysis. Combining AHP with GIS provides an effective approach for studies of mineral potential mapping evaluation. Selection of potential areas for exploration is a complex process in which many diverse criteria are to be considered. In this article, AHP and GIS are used for providing potential maps for Cu porphyry mineralization on the basis of criteria derived from geologic, geochemical, and geophysical, and remote sensing data including alteration and faults. Each criterion was evaluated with the aid of AHP and the result mapped by GIS. This approach allows the use of a mixture of quantitative and qualitative information for decision-making. The results of application in this article provide acceptable outcomes for copper porphyry exploration.

Remote sensing extraction of geological anomalies is one of the important research in GARS.Using ETM+ image rectification, image mosaic, color composite, color space transformation,kids of image enhancement processing to extract structure information. Removal of interference information by mask, with principal component analysis and threshold segmentation technique for iron staining and hydroxy alteration information extraction.Four mineral potential targets has been delineated when synthetically analysising of geological information.The predicting the mineralization above provides a reference and effective way to accelerate geological prospecting work in Laos.

The aim of the present study is to analyze relationships between epithermal Au-Ag deposits of the hydrothermal type and related geological factors and integrate the relationships using probabilistic and statistical models in a geographic information system (GIS) environment. A variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential Au and Ag deposits in the Gangreung area, Korea. This empirical approach assumes that all deposits shared a common genesis. The method consists of three main steps: (i) identification of spatial relationships; (ii) quantification of such relationships and (iii) integration of multiple quantified relationships. A spatial database containing Au and Ag deposits, topographic, geologic, geophysical and geochemical data was constructed using a GIS. The factors relating to 103 Au and Ag mineral deposits are the geological data such as lithology and fault structure, geochemical data including the abundance of Al, As, Ba, Ca, Cd, Co, conductivity, Cr, Cu, Eh, Fe, HCO3–, K, Li, Mg, Mn, Mo, Na, Ni, Pb, pH, Si, Sr, V, W, Zn, Cl−, F−, PO43−, NO2−, NO3− and SO42−, and geophysical data including Bouguer and magnetic anomalies. Using the constructed spatial database, the relationships between mineral deposit areas and 36 related factors are identified and quantified by probabilistic and statistical modeling; that is, likelihood ratio, weights of evidence and logistic regression. All the factors were combined to produce a map of the regional mineral potential using the overlay method in a GIS environment. The mineral potential map was then verified by comparison with known mineral deposits. The verification results give respective accuracies of 82.52%, 72.45% and 81.60% for the likelihood ratio, weights of evidence and logistic regression models, respectively. The mineral potential map can be used as a source of basic information for mineral resource development.

This paper presents a Geographic Information System (GIS)-based spatial analysis scheme to account for spatial patterns and association in geological thematic mapping with multiple geological data sets. The multi-buffer zone analysis, the main part of the present study, was addressed to reveal the spatial pattern around geological source primitives and statistical analysis based on a contingency table was performed to extract information for the assessment of an integrated layer. Mineral potential mapping using multiple geological data sets from Ogdong in Korea was carried out to illustrate application of this methodology. The results obtained from the case study indicated that some geochemical elements and residual magnetic anomaly dominantly affected spatial patterns of the mineral potential map in the study area and the dominant classes of input data layers were also extracted. This information on spatial patterns of multiple geological data sets around mines could be used as effective evidences for the interpretation of the integrated layer within GIS.

Guidelines for mineral exploration normally are based on models for a given type of ore deposit. These guidelines usually are based on descriptive and metallogenetic data, and on expertise judgment. This paper presents a comparison between results produced by two of these methods: weights-of-evidence and fuzzy logic. The mineral favorability maps for gold exploration were produced in a Geographic Information Systems (GIS) environment and took into account five sources of data and information: (i) satellite images; (ii) a geochemical survey; (iii) an airborne geophysical survey; (iv) geo-structural mapping; and (v) ground elevation. These data and information were integrated through a conceptual model developed for gold mines and occurrences in the studied region. Both favorability maps highlighted the known gold occurrences and validated the approach. High gold potential areas highlighted by both maps show good correlation. But, the weights-of-evidence method delineated smaller highly favorable areas compared to the fuzzy logic map. On the other hand, the weights-of-evidence method produced higher biased probability within the favorable zones when compared with fuzzy logic methods favorable areas. New exploration targets were identified and should be further investigated.

The geologic features indicative of Cu, Pb, Zn mineral deposits in a area are fractures (structure), and host rock sediments. Datasets used include Cu, Pb, Zn deposit points record, geological data, remote sensing imagery (Landsat TM5). The mineral potential of the study area is assessed by means of GIS based geodata integration techniques for generating predictive maps. GIS predictive model for Cu, Pb, Zn potential was carried out in this study area (Weixi) using weight of evidence.

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.

Numerous small to medium sized deposits of base metals, gold, and uranium, ranging in age from Precambrian to Tertiary, occur in South Australia. In addition, there are significant deposits of coal, iron ore and industrial minerals, which are currently being exploited. However, the mineral potential of the State is best illustrated by the presence of the giant polymetallic Olympic Dam deposit, and large areas of rocks equivalent to those hosting the Broken Hill silver-lead-zinc deposits, located across the border in New South Wales. The majority of South Australia is underlain by prospective Precambrian crustal blocks, which sporadically outcrop, form extensive subcrop, or are concealed by a thin veneer of Recent cover. Exploration for minerals in the State has been hindered by the generally poor outcrop, creating a challenge for mineral explorers. Meeting this challenge will require the extensive use of geophysical exploration methods.

This article examines the Russian non-fuel mineral sector regulatory system. It contains regulatory related information and analysis generally useful to a foreign investor interested in Russia's mineral potential. The regulatory system in the Russian Federation is undergoing substantial change and laws that apply to minerals are evolving at a relatively fast pace. Two possible exploration and mining investment approaches are now possible in the Russian Federation: authorisation under the national mining law, and investment pursuant to the production sharing agreement law. The lack of substantial foreign mining investment in Russia is attributable, at least in part, to perceived shortcomings in the legal system. Due to the transitional nature of the Russian Federation legal system, the low level of precedent foreign mineral investment in Russian, fragmentary legal reporting, and the untested nature of ambiguously worded legislation, the information and analysis contained here is, by necessity, speculative.

This paper describes a GIS-based application of a radial basis functional link net (RBFLN) to map the potential of SEDEX-type base metal deposits in a study area in the Aravalli metallogenic province (western India). Available public domain geodata of the study area were processed to generate evidential maps, which subsequently were encoded and combined to derive a set of input feature vectors. A subset of feature vectors with known targets (i.e., either known mineralized or known barren locations) was extracted and divided into (a) a training data set and (b) a validation data set. A series of RBFLNs were trained to determine the network architecture and estimate parameters that mapped the maximum number of validation vectors correctly to their respective targets. The trained RBFLN that gave the best performance for the validation data set was used for processing all feature vectors. The output for each feature vector is a predictive value between 1 and 0, indicating the extent to which a feature vector belongs to either the mineralized or the barren class. These values were mapped to generate a predictive classification map, which was reclassified into a favorability map showing zones with high, moderate and low favorability for SEDEX-type base metal deposits in the study area. The method demarcates successfully high favorability zones, which occupy 6% of the study area and contain 94% of the known base metal deposits.

A back-propagation artificial neural network (ANN) model is proposed to discriminate zones of high mineral potential in the Rodalquilar gold field, south-east Spain, using remote sensing and mineral exploration data stored in a GIS database. A neural network model with three hidden units was selected by means of the k -fold cross-validation method. The trained network estimated a gold potential map efficiently, indicating that both previously known and unknown potentially mineralized areas can be detected. These initial results suggest that ANN can be an effective tool for mineral exploration spatial data modelling.

Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.

The 1785 km2 Takab area of northwest Iran is a region of high mineral potential but difficult access for which an innovative exploration approach is required. Interpretation of aeromagnetic, Landsat TM, geological and mineral occurrence data are used to recognize a combination of mapped geological features, spectral characteristics, and magnetic signatures that could be associated with epithermal gold, arsenic, antimony, and base metal deposits near Takab. Geological data such as permeable, reactive host rocks, surface igneous rocks, and structures are integrated with interpretations of remotely sensed data of hydrothermal alteration and subsurface igneous heat sources. Four binary maps representing diagnostic deposit recognition criteria were combined in a weights-of-evidence model, which uses the spatial distribution of 19 known mineral occurrences to calculate a final map of further gold and base metal potential in the Takab area.

With the dissolution of the former Soviet Union in 1991, the nations of Central Asia gained independence and began the transition to market driven economies. Both the political and economic transformations of the Central Asian Republics (CARs) (Kazakstan, Kyrgyzstan, Tajikistan and Uzbekistan) have been difficult primarily because of a holdover of various centrally planned `command and control' approaches to national development. The extensive mapping and exploration of the CARs during the Soviet era (approximately 1921–1991) led to the discovery of several hundreds of mineral deposits that were subsequently evaluated, and it is this `pool' of known, but undeveloped, deposits which is of major interest to foreign and domestic companies. However, foreign investment in the mineral sectors of the CARs is at best quite modest and there are very few positive signs that foreign investment will increase dramatically in the near term. The major risks that the mining industry faces as it works in the CARs arise primarily from the fact that the nations are transitional economies which lack both a comprehensive legal framework and experience in dealing with foreign mining corporations and their practices. The major risks are political, economic, financial, infrastructure, contractual, environmental, social and cultural and workforce related. If all individual risk factors are weighted equally, the relative ranking of the countries (from least risk to most risk for mineral development) would be Kyrgyzstan, Kazakstan, Uzbekistan and Tajikistan respectively. However, such a classification scheme does not effectively weight the importance of mineral potential. If the risk criteria are weighted with respect to geologic potential, the ranking of the countries would be (from least risk to most risk) Kazakstan, Uzbekistan, Kyrgyzstan and Tajikistan, respectively.

This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.

Mineral exploration is a multidisciplinary task requiring the simultaneous consideration of numerous disparate geophysical, geological, and geochemical datasets (Knox-Robinson, 2000). The size and complexity of regional exploration data available to geologist are increasing rapidly from a variety of sources such as remote sensing, airbone geophysics, large commercially available geological and geochemical data (Brown et al., 2000). This demands more effective integration and analysis of regional and various of geospatial data with different formats and attributes. In addition, this needs spatial modeling techniques using observations regarding the association of mineral occurrences with various geological features in a qualitative manner. Geographic Information System (GIS) methods are very useful for processing and combining data within maps in mineral potential mapping. The development of GIS-based methods for integration and analysis of regional exploration datasets has an important role in assisting the decision-making processes for geologists in selection of exploration area (Brown et al., 2000). More recently, the mineral exploration industry has taken this approach further and with the help of spatial data modeling in GIS (Partington, 2010). The spatial modeling techniques been proposed for mineral potential mapping, such as weights of evidence model (Bonham-Carter et al., 1988, 1989; Agterberg et al., 1990; Xu et al., 1992; Rencz et al., 1994; Pan, 1996; Raines, 1999; Carranza & Hale, 2000; Tangestani & Moore, 2001; Carranza, 2004; Agterberg & Bonham-Carter, 2005; Jianping et al., 2005; Nykanen & Raines, 2006; Porwal et al., 2006; Roy et al., 2006; Nykanen & Ojala, 2007; Raines et al., 2007; Oh & Lee, 2008; Harris et al., 2008; Benomar et al., 2009), Bayesian network classifiers (Porwal et al., 2006), logistic regression (Chung and Agterberg, 1980; Agterberg, 1988; Oh & Lee, 2008), fuzzy logic (An et al., 1991; Bonham-Carter, 1994; Eddy et al., 1995; D’Ercole et al., 2000; Knox-Robinson, 2000; Luo & Dimitrakopoulos, 2003; De Quadros et al., 2006; Carranza et al., 2008; Nykanen, 2008), artificial neural networks (Singer & Kouda, 1996; Harris & Pan, 1999; Brown et al., 2000, 2003; Rigol-Sanchez et al., 2003; Behnia, 2007; Skabar, 2007; Oh & Lee, 2008), and an evidence theory model (Moon, 1990, 1993; An & Moon, 1993; Moon & So, 1995; Porwal et al., 2003; Carranza et al., 2005). Researches using GIS have involved comparison of methods (Harris et al., 2003; Oh & Lee, 2008) and resolutions of spatial data used for mapping mineral potential, development of advanced methods

The purpose of this study is to map mineral potential in Gangreung area, Korea. The mineral potential map was made and validated using likelihood ratio, logistic regression and artificial neural network models with a geographic information system (GIS). Moreover integration of the models has applied to get the better accuracy than each model. For this, the factors related to Au-Ag mineral occurrence were compiled in the GIS database. The factors are the geological data of lithology and fault structure, geochemical data. Using these factors, the potential of mineral were analysed using the 3 models. The validation result showed that the likelihood ratio, logistic regression and artificial neural network models had 83.70%, 81.91% and 77.37% accuracies. But the integrated mineral potential map, prediction accuracy was 92.94%. The generated maps could be used to not only predict known areas of Au-Ag occurrence, but also identify areas of potential mineralization where no known deposit occurs.

The geochemistry is key mineral information to identify mineral potential targets. Exploration geochemistry continues to be a major contributor to mineral exploration programs at scales ranging from regional reconnaissance down to mining leases. In this paper, a combination of geographic information system (GIS) technology and fractal method is applied to map the mineral potential in the case study area, western Damingshan region in Guangxi, the latter can make the best use of the visualization, spatial analysis, and management function of GIS to derive the anomaly information of exploration geochemistry associated with mineral deposits. The effective anomaly targets can be validated by GIS platform combined geological, geochemical, and digital elevation modeling (DEM) data, and the research results show that the combined technology is effective to map the mineral potential targets in study area.

This paper presents a quantitative method for mineral exploration combining of the three multi-class geological variables aided by GIS spatial analysis techniques and statistical analysis. The process of constructing a mineral potential map based on extended weights-of-evidence modeling to investigate the relationship between multi-class evidential map patterns and known gold deposits in the Wulonggou gold district of Qinghai Province. Finally, the study area was classified into three target zones of high potential areas, moderate potential areas and low potential areas based on the sum of weights that avoid the affect of conditional independence. The high potential areas occupy 2.6% of the total area and contain 76.5% of the known deposits, reaching a better prediction rate.

The mineral potential of West Africa has been the impetus for foreign direct investment (FDI) in the sub-region. In a bid to strengthen FDI flow, the World Bank, following the collapse of public-controlled enterprises, recommended economic policies, which favoured the foreign investors and consequently led to the influx of FDI.

Nunavut has gained importance in the last few years as an area of high mineral potential with exploration leading to discoveries of several mineral deposits that have, or will in the coming years, become mines. As a territory with an economy based in large part on government employment, new mining operations have the potential to provide Nunavut with an alternative way to develop its economy through job creation, local business opportunities, royalties, and taxes. Mining could provide the people of Nunavut with training opportunities for various jobs that can later be used for employment with community-based businesses. Understanding the socio-economic impacts of past Arctic mining operations, such as the Polaris and Nanisivik Mines, is important if these opportunities are to become reality and if Nunavut is to benefit as much as possible from future mining operations.

Predicting minerals potential helps miners in making wise decisions. In this paper, we present a computerized prototype system called GoldXplorer that enables to predict the existence and distribution of gold (aurum (Au)) at a given location with its geological and geographical factors. With its two intelligent engines based on back-propagation neural network and self-organizing map techniques, GoldXplorer helps users (geologists, investors, gold miners or individuals) to determine the gold potentials for further investigation and mining activity. For the case study, we use data sets collected from an area called Kuala Lipis in Malaysia. This set of data was supplied and verified by Malaysia Department of Minerals and Geosciences. The data consists of the location (RSOE and RSON) and all types of minerals found in the area. Two thousand sample points were used in this research and it results in eighty percent (or more) of accuracy rate. With the results produced by GoldXplorer, the potential strategic locations for gold mining may be determined. In addition, with the predicted information, future planning can be carried out so that the earth can be properly kept from any illegal mining.

Image fusion can be divided into pixel, feature and decision level. GIS based methods for mineral exploration are commonly based on feature and decision level. In this paper, a Spatially Weighted Principal Component Analysis (SWPCA) method was applied to combine multi-scale and multi-source geo-information at pixel level. The combination of various geo-information data sets will be either spectrally or spatially based. Results obtained by applying SWPCA to geophysical & remote sensing data and geochemical & remote sensing data clearly show the spatial distribution of intrusive bodies and mineral deposits related anomalies, respectively.

This report compiles selected results from 13 U.S. Geological Survey (USGS) mineral resource assessment studies conducted in Idaho and Montana into consistent spatial databases that can be used in a geographic information system. The 183 spatial databases represent areas of mineral potential delineated in these studies and include attributes on mineral deposit type, level of mineral potential, certainty, and a reference. The assessments were conducted for five 1° x 2° quadrangles (Butte, Challis, Choteau, Dillon, and Wallace), several U.S. Forest Service (USFS) National Forests (including Challis, Custer, Gallatin, Helena, and Payette), and one Bureau of Land Management (BLM) Resource Area (Dillon). The data contained in the spatial databases are based on published information: no new interpretations are made. This digital compilation is part of an ongoing effort to provide mineral resource information formatted for use in spatial analysis. In particular, this is one of several reports prepared to address USFS needs for science information as forest management plans are revised in the Northern Rocky Mountains.

The GIS (Geographic Information System) Central Asia is composed of spatially referenced geographical, geological, geophysical, geochemical and mineral deposit thematic layers, and their respective attribute data. It is issued to establish insights in the regions mineral potential and its past and future mining activities. Subsequently, the information system is further exploited to derive new rules between the different attribute information in their relation to mineral deposit information and the special distribution of the deposits.

Mineral potential mapping is the process of producing a map that depicts the favorability of mineralization occurring over a specified region. The map should reflect the location of known mineral occurrences and also predict the distribution of areas of high mineral potential where little or no mining activity currently exists. Although the development of geographic information system technology and digital data manipulation techniques has enabled mineral exploration geologists to make more efficient use of resource information, many of the methods used are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a mineral deposition model. This paper describes a data-driven mineral potential mapping technique based on feed-forward neural networks. Results are provided from applying the technique to gold exploration in a region of South West Victoria, Australia, using a range of geological, geophysical and geochemical input variables.

On 22 May 1990 the Yemen Arab Republic and Peoples Democratic Republic of Yemen were unified to form the Republic of Yemen, with a population of about 14 million people in an area of about 580 000 km2. The Republic of Yemen is hoping to develop its mineral industry. Possessing a well established geological database plus a newly issued mining law and investment law, Yemen is inviting all investors, whether from the Yemeni private sector or foreign companies, either solely or in joint ventures to investigate opportunities in minerals and construction and industrial materials.

The endeavors of local governments, of foreign and international institutions, as well as those efforts of private companies to evaluate the energy and mineral resources of the Central American and Caribbean countries, reveal a wide divergence in the results obtained for each individual country. These differences are due to factors or situations that have conditioned, stimulated, or limited the endeavors. Among these factors are the differences in the customs that characterize the Latin American countries, particularly those in Central America and the Caribbean, and also the negative attitudes of some governments toward the efforts of outsiders to study their resources. Of particular concern are those governments which have been unable to understand the importance of obtaining a potential resource base as a key factor in the economic development of their countries. Also pertinent to this problem has been the lack of native professional people and trained personnel able to carry out and give continuity to those programs that have been devised to evaluate the energy and mineral potential of the country. Of great significance to the success of such programs is the need for establishing and properly financing the governmental institutions that are to produce and eventually publish the data base required for planning the economic development of a country. Efforts to obtain an adequate data base for economic development in the countries of Central America, the area with which the author is familiar, are here considered.

This paper is designed to set the general stage for the technical papers on resources that follow. Any inquiry into the mineral potential of the Caribbean Basin needs to consider as a point of departure what mineral fuels (oil, gas, and coal), metallic minerals, and industrial minerals are already being produced in the Caribbean Basin countries.

Among the Himalayan edible plants, Spondias pinnata Linn. (Local Name: Amara) grows naturally and sparsely distributed in the forest between 500 to 1500 m altitude. It has been identified recently a high priority bioresource with outstanding economic and ecological value. Therefore, the study have been undertaken to assess the nutritive and mineral potential of ripe fruits of S. pinnata and their comparison with other wild as well as horticulture fruits. The fruit of S. pinnata possess food energy 189–203 kcal/g, crude fat 12.23–12.54%, crude fiber 3.13–4.03%, total carbohydrate 23.54–16.30%, sodium 1.38–0.96%, calcium 0.93- 0.15%, iron 1.5–1.3%, and copper 0.9–1.23 0%, respectively. Results of the present study indicated that the nutritive and mineral potentials of S. pinnata is higher as compared to other wild edibles of the region and comparable with commercially available horticulture fruits.

The US Bureau of Mines and the US Geological Survey appraised the mineral resources and mineral resource potential of this area that is about 45 mi west of Salt Lake City. Inferred subeconomic resources of sand and gravel and of limestone suitable for use in making cement exist within this study area, but these are not likely to be developed. There are no other identified resources in this study area. Roughly the southern half and the northeastern corner of this study area have a moderate mineral resource potential for undiscovered lead, zinc, silver, gold, and mercury in vein and replacement deposits and for undiscovered sediment-hosted, disseminated gold resources. The remaining parts of this study area are assigned a low mineral potential for these types of resources. A small area at the southwestern corner of this study area may contain thermal waters and is assigned a moderate potential for undiscovered geothermal resources. This entire study area has a low potential for oil and gas resources.