Weight Of Evidence Model Research Articles

A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

Abstract This study evaluated and compared landslide susceptibility maps produced with three different methods, frequency ratio, weights of evidence, and logistic regression, by using validation datasets. The field surveys performed as part of this investigation mapped the locations of 90 landslides that had been identified in the Sultan Mountains of south-western Turkey. The landslide influence parameters used for this study are geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transportation capacity index, distance to drainage, distance to fault, drainage density, fault density, and spring density maps. The relationships between landslide distributions and these parameters were analysed using the three methods, and the results of these methods were then used to calculate the landslide susceptibility of the entire study area. The accuracy of the final landslide susceptibility maps was evaluated based on the landslides observed during the fieldwork, and the accuracy of the models was evaluated by calculating each model’s relative operating characteristic curve. The predictive capability of each model was determined from the area under the relative operating characteristic curve and the areas under the curves obtained using the frequency ratio, logistic regression, and weights of evidence methods are 0.976, 0.952, and 0.937, respectively. These results indicate that the frequency ratio and weights of evidence models are relatively good estimators of landslide susceptibility in the study area. Specifically, the results of the correlation analysis show a high correlation between the frequency ratio and weights of evidence results, and the frequency ratio and logistic regression methods exhibit correlation coefficients of 0.771 and 0.727, respectively. The frequency ratio model is simple, and its input, calculation and output processes are easily understood. The interpretations of the susceptibility map reveal that geology, slope steepness, slope aspect, and elevation played major roles in landslide occurrence and distribution in the Sultan Mountains. The landslide susceptibility maps produced from this study could therefore assist planners and engineers during development and land-use planning.

LAND COVER CLASSIFICATION OF MULTI-SENSOR IMAGES BY DECISION FUSION USING WEIGHTS OF EVIDENCE MODEL

Abstract. This paper proposed a novel method of decision fusion based on weights of evidence model (WOE). The probability rules from classification results from each separate dataset were fused using WOE to produce the posterior probability for each class. The final classification was obtained by maximum probability. The proposed method was evaluated in land cover classification using two examples. The results showed that the proposed method effectively combined multisensor data in land cover classification and obtained higher classification accuracy than the use of single source data. The weights of evidence model provides an effective decision fusion method for improved land cover classification using multi-sensor data.

The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling

The Yushu County, Qinghai Province, China, April 14, 2010, earthquake triggered thousands of landslides in a zone between 96°20′32.9″E and 97°10′8.9″E, and 32°52′6.7″N and 33°19′47.9″N. This study examines the use of geographic information system (GIS) technology and Bayesian statistics in creating a suitable landslide hazard-zone map of good predictive power. A total of 2,036 landslides were interpreted from high-resolution aerial photographs and multi-source satellite images pre- and post-earthquake, and verified by selected field checking before a final landslide-inventory map of the study area could be established using GIS software. The 2,036 landslides were randomly partitioned into two subsets: a training dataset, which contains 80 % (1,628 landslides), for training the model; and a testing dataset 20 % (408 landslides). Twelve earthquake triggered landslide associated controlling parameters, such as elevation, slope gradient, slope aspect, slope curvature, topographic position, distance from main surface ruptures, peak ground acceleration, distance from roads, normalized difference vegetation index, distance from drainages, lithology, and distance from all faults were obtained from variety of data sources. Landslide hazard indices were calculated using the weight of evidence model. The landslide hazard map was compared with training data and testing data to obtain the success rate and predictive rate of the model, respectively. The validation results showed satisfactory agreement between the hazard map and the existing landslide distribution data. The success rate is 80.607 %, and the predictive rate is 78.855 %. The resulting landslide hazard map showed five classes of landslide hazard, i.e., very high, high, moderate, low and very low. The landslide hazard evaluation map should be useful for environmental recovery planning and reconstruction work.

Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region

Tens of thousands of landslides were triggered by May 12, 2008 earthquake over a broad area. The main purpose of this article is to apply and verify earthquake-triggered landslide hazard analysis techniques by using weight of evidence modeling in Qingshui (清水) River watershed, Deyang (德阳) City, Sichuan (四川) Province, China. Two thousand three hundred and twenty-one landslides were interpreted in the study area from aerial photographs and multi-source remote sensing imageries post-earthquake, verified by field surveys. The landslide inventory in the study area was established. A spatial database, including landslides and associated controlling parameters that may have influence on the occurrence of landslides, was constructed from topographic maps, geological maps, and enhanced thematic mapper (ETM+) remote sensing imageries. The factors that influence landslide occurrence, such as slope angle, aspect, curvature, elevation, flow accumulation, distance from drainages, and distance from roads were calculated from the topographic maps. Lithology, distance from seismogenic fault, distance from all faults, and distance from stratigraphic boundaries were derived from the geological maps. Normalized difference vegetation index (NDVI) was extracted from ETM+ images. Seismic intensity zoning was collected from Wenchuan (汶川) Ms8.0 Earthquake Intensity Distribution Map published by the China Earthquake Administration. Landslide hazard indices were calculated using the weight of evidence model, and landslide hazard maps were calculated from using different controlling parameters cases. The hazard map was compared with known landslide locations and verified. The success accuracy percentage of using all 13 controlling parameters was 71.82%. The resulting landslide hazard map showed five classes of landslide hazard, i.e., very high, high, moderate, low, and very low. The validation results showed satisfactory agreement between the hazard map and the existing landslides distribution data. The landslide hazard map can be used to identify and delineate unstable hazard-prone areas. It can also help planners to choose favorable locations for development schemes, such as infrastructural, buildings, road constructions, and environmental protection.

Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping

The aim of this study is to analyze the relationship among groundwater productivity data including specific capacity (SPC) and transmissivity (T) as well as its related hydrogeological factors in a bedrock aquifer, and subsequently, to produce the regional groundwater productivity potential (GPP) map for the area around Pohang City, Korea using a geographic information system (GIS) and a weights-of-evidence (WOE) model. All of the related factors, including topography, lineament, geology, forest, and soil data were collected and input into a spatial database. In addition, SPC and T data were collected from 83 and 81 well locations, respectively. Four dependent variables including SPC values of ≥6.25 m3/d/m (Case 1) and T values of ≥3.79 m2/d (Case 3) corresponding to a yield (Y) of ≥500 m3/d, and SPC values of ≥3.75 m3/d/m (Case 2) and T values of ≥2.61 m2/d (Case 4) corresponding to a Y of ≥300 m3/d were also input into a spatial database. The SPC and T data were randomly selected in an approximately 70:30 ratio to train and validate the WOE model. Tests of conditional independence were performed for the used factors. To assess the regional GPP for each dependent variable, W+ and W- of each factor's rating were overlaid spatially. The results of the analysis were validated using area under curve (AUC) analysis with the existing SPC and T data that were not used for the training of the model. The AUC of Cases 1, 2, 3 and 4 showed 0.7120, 0.6893, 0.6920, and 0.7098, respectively. In the case of the dependent variables, Case 1 had an accuracy of 71.20% (AUC: 0.7120), which is the best result produced in this analysis. Such information and the maps generated from it could be used for groundwater management, a practice related to groundwater resource exploration.

GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison

Summary In this study, groundwater spring potential maps produced by three different methods, frequency ratio, weights of evidence, and logistic regression, were evaluated using validation data sets and compared to each other. Groundwater spring occurrence potential maps in the Sultan Mountains (Konya, Turkey) were constructed using the relationship between groundwater spring locations and their causative factors. Groundwater spring locations were identified in the study area from a topographic map. Different thematic maps of the study area, such as geology, topography, geomorphology, hydrology, and land use/cover, have been used to identify groundwater potential zones. Seventeen spring-related parameter layers of the entire study area were used to generate groundwater spring potential maps. These are geology (lithology), fault density, distance to fault, relative permeability of lithologies, elevation, slope aspect, slope steepness, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport capacity index, drainage density, distance to drainage, land use/cover, and precipitation. The predictive capability of each model was determined by the area under the relative operating characteristic curve. The areas under the curve for frequency ratio, weights of evidence and logistic regression methods were calculated as 0.903, 0.880, and 0.840, respectively. These results indicate that frequency ratio and weights of evidence models are relatively good estimators, whereas logistic regression is a relatively poor estimator of groundwater spring potential mapping in the study area. The frequency ratio model is simple; the process of input, calculation and output can be readily understood. The produced groundwater spring potential maps can serve planners and engineers in groundwater development plans and land-use planning.

A multidisciplinary weight of evidence approach for classifying polluted sediments: Integrating sediment chemistry, bioavailability, biomarkers responses and bioassays

Evaluation of chemical bioavailability and onset of biological alterations is fundamental to assess the hazard of environmental pollutants, particularly when associated to sediments which need to be removed. In the present work, five sediment samples were collected from the Venice Lagoon and data from sediment chemistry were integrated with those of bioaccumulation of chemicals in European eel ( Anguilla anguilla) exposed under laboratory conditions, responses of a wide battery of biomarkers, and standardized ecotoxicological bioassays. The overall results were elaborated within a recently developed, software-assisted weight of evidence (WOE) model which provides synthetic indices for each of considered line of evidence (LOE), before a general evaluation of sediment hazard. Levels of chemicals in sediments were not particularly elevated when compared to sediment quality guidelines of Venice Protocol. On the other hand, bioavailability was evident in some samples for Cd, Cu, Zn and, especially, polycyclic aromatic hydrocarbons. The ecotoxicological approach provided further evidence on the biological and potentially harmful effects due to released contaminants, and oxidative-mediated responses appeared of primary importance in modulating sublethal responses and the onset of cellular alterations. Biomarkers variations were sensitive, and more evident variations included significant changes of cytochrome P450 biotransformation pathway, antioxidant responses, onset of oxidative damages, lysosomal membrane stability and genotoxic effects. The results obtained from the battery of bioassays indicated that responses measured at organism level were in general accordance but less marked compared to the onset of sublethal changes measured through biomarkers. Overall this study revealed differences when comparing evaluations obtained from different LOEs, confirming the importance of considering synergistic effects between chemicals in complex mixtures. Compared to a qualitative pass–fail approach toward normative values, the proposed WOE model allowed a quantitative characterization of sediment hazard and a better discrimination of on the basis of various types of chemical and biological data.

Integration of ground subsidence hazard maps of abandoned coal mines in Samcheok, Korea

Integrated techniques were developed, applied, and validated for the analysis of ground subsidence hazards by abandoned coal mines in Samcheok, Korea, using a geographic information system (GIS). Slope, depth of drift, distance from drift, groundwater level, permeability, geology, and land use were extracted or calculated from the digital elevation model, topographic, drift distribution, borehole, geologic, and land use database. By using the constructed spatial database, the relations between the ground subsidence location and seven related factors were identified and quantified by frequency ratio (FR), weights-of-evidence (WOE), logistic regression (LR), and artificial neural network (ANN) models. The relations were used as factor ratings in the overlay analysis to create ground subsidence hazard indices and maps. The four ground subsidence hazard maps were reflected as the new input factors and integrated using FR, WOE, LR, and ANN models to make a hazard map. All of the subsidence hazard maps were validated by comparison with known ground subsidence locations that were not used in the analysis. As a result, the integrated ground subsidence hazard maps used four new subsidence-related input factors that showed a greater accuracy (96.46% for FR, 97.22% for WOE, 97.20% for LR, and 96.70% for ANN, respectively), than the individual ground subsidence maps (95.54% for FR, 94.22% for WOE, 96.89% for LR, and 94.45% for ANN, respectively) using the first seven factors.

Binary Pattern Recognition in the Presence of Correlated Multiple Dependent Variables

In binary spatial pattern recognition, there are many situations where the researcher could be interested in a number of dependent variables that are themselves correlated. For instance, different types of crime often coexist in the same area, or different species could share the same habitat. In cases like these, a natural correlation exists amongst the dependent variables of interest and is informative for spatial probability mapping. Weights of evidence (WE) modelling is a popular Bayesian probability method for binary pattern recognition, but it only deals with one single dependent variable at a time and ignores the correlation between the dependent variables. In this article, a multiple dependent variable weights of evidence (MDVWE) model will be developed. It will be shown that the new MDVWE model can be viewed as a restricted version of the conditional dependence-adjusted weights of evidence (CDAWE) model of Deng (Nat Resour Res 18(4):249–258, 2009). The MDVWE model is easy to program and implement. By means of a simulation study, it will be shown that the MDVWE model outperforms the traditional WE model both in terms of in-sample fit and out-of-sample prediction accuracy.

Weight of Evidence Allocates Mineral Depositional Zones

Problem statement: Yunnan province in general and Pulang area in part icular is geological rich area which prevents field study in multi locat ions due to high risk outcrop. Approach: New technology such as Geographic Information System (GIS) and an ArcView extension module Arc- weight of evidence (WofE) became very handy to provide safety for researchers and allow organization to control their budget. Results: In order to guide mineral exploration, to achieve t he purpose of rapid evaluation of mineral resources a serial of modeled prediction methods were established. Weight of evidence model is to predic t the existent thing by combining the known evidence of the study area, the importance of evide nce is determined based on statistical method. Contrary to the fuzzy logic method, it avoids the s ubjective selection of evidence and the subjective evaluation of evidence. The weight of evidence can determine the weight in the same standard conditions (using known mine sites as guidance data ), so that the variables can be compared in the united scale, a higher reliability. Conclusion/Recommendations: Comparing predicted and known distribution patterns of porphyry, most mine sites are located in the areas with high posterior probability, forecast area accounts for 11.5% of th e entire study area. Predicted results show clearly that the boundary of potential areas and the non-po tential areas is clear. Therefore, fuzzy logic and other methods should be applied to predict the resu lts for further comparison. More accurate prediction would draw a big smile on faces of share holders.

An Ordered Weights of Evidence Model for Ordered Discrete Variables

The standard Weights of Evidence (WE) model produces probability estimates for the presence of binary events. However, in many empirical studies the discrete event of interest can take on ordered values. For instance, the presence of mineral deposits may be classified further into different grades. In this paper, a new Ordered Weights of Evidence (OWE) model will be developed. Borrowing the conceptual framework of the latent variable interpretation of the standard Ordered Logistic Regression (OLR) model, the OWE can produce probability estimates for the presence of ordered discrete events. It will be shown that the OWE is computationally less intensive than the OLR. Through a simulation study, it will be shown that the OWE is comparable to the OLR both in terms of in-sample fit and out-of-sample forecasts.

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

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

A Spatially Autocorrelated Weights of Evidence Model

One of the most important features of spatial datasets is that they often exhibit spatial autocorrelation, where locational similarities are observed jointly with similarities in values. Both logistic regression (LR) modelling and weights of evidence (WE) modelling are methods commonly applied in binary pattern recognition. While a spatially autocorrelated variant of the LR model, the so-called autologistic regression (ALR) model, exists in the literature, a spatially autocorrelated variant of the WE model does not exist. In this paper, a spatially autocorrelated weights of evidence (SACWE) model will be proposed. It will be demonstrated that the new model contains the same amount of spatial information as does an ALR model, and it is easy to program and implement. Via a simulation study, it will be shown that, in the presence of spatial autocorrelation, both in terms of in-sample fit and out-of-sample predictions the SACWE model is on par with the ALR model, while significantly outperforming the conventional WE model.

Spatial modelling of socioeconomic data to understand patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain)

The majority of wildfires in the Mediterranean Basin are caused directly or indirectly by human activity. Many biophysical and socioeconomic factors have been used in quantitative analyses of wildfire risk. However, the importance and effects of socioeconomic factors in spatial modelling have been given inadequate attention. In this paper, we use different approaches to spatially model our data to examine the influence of human activity on wildfire ignition in the south west of the Madrid region, central Spain. We examine the utility of choropleth and dasymetric mapping with both Euclidean and functional distance surfaces for two differently defined wildfire seasons. We use a method from Bayesian statistics, the Weights of Evidence model, and produce ten predictive maps of wildfire risk: (1) five maps for a two-month fire season combining datasets of evidence variables and (2) five maps for the four-month fire season using the same dataset combinations. We find that the models produced from a choropleth mapping approach with spatial variables using Euclidian and functional distance surfaces are the best of the ten models. Results indicate that spatial patterns of wildfire ignition are strongly associated with human access to the natural landscape. We suggest the methods and results presented will be useful to optimize wildfire prevention resources in areas where human activity and the urban–forest interface are important factors for wildfire ignition.

A Conditional Dependence Adjusted Weights of Evidence Model

One of the key assumptions in weights of evidence (WE) modelling is that the predictor patterns have to be conditionally independent. When this assumption is violated, WE posterior probability estimates are likely to be biased upwards. In this paper, a formal expression for the bias of the contrasts will be derived. It will be shown that this bias has an intuitive and convenient interpretation. A modified WE model will then be developed, where the bias is corrected using the correlation structure of the predictor patterns. The new model is termed the conditional dependence adjusted weights of evidence (CDAWE) model. It will be demonstrated via a simulation study that the CDAWE model significantly outperforms the existing WE model when conditional independence is violated, and it is on par with logistic regression, which does not assume conditional independence. Furthermore, it will be argued that, in the presence of conditional dependence between predictor patterns, weights variance estimates from WE are likely to understate the true level of uncertainty. It will be argued that weights variance estimates from CDAWE, which are also bias-corrected, can properly address this issue.

A predictive GIS model for potential mapping of copper, lead, and zinc in Langping area, China

Mineral resource potential mapping is a complex analytical process, which requires the consideration and the integration of a number of spatial evidences like geological, geomorphological, and wall rock alteration. The aim of this paper is to establish mineral exploration model for copper, lead, and zinc in Lanping basin area using the capability of analytical tools of Geographic Information System (GIS) and remote sensing data to generate maps showing favorable mineralized area. The geo-exploration dataset used for the research includes copper, lead, and zinc deposits, geological maps, topographic maps, structural maps, and ETM+ imagery. Geological features indicative of potential copper, lead, and zinc were extracted from the datasets input in the predictive model. The method of weights of evidence modeling is a probability-based technique for generating mineral potential maps using the spatial distribution of indicative features with respect to the known mineral occurrences. The method of weights of evidence probabilistic modeling provides a quantitative method for delineating areas with potential of copper, lead, and zinc mineral deposits in the Lanping Basin area. weights (W+, W−) and contrast (C=(W+)-(W−)) calculations guide the data-driven modeling. The four most important spatial features for exploration guide for copper, lead, and zinc mineralization hosted in the Lanping Basin area are alteration zones, faults, host rocks, and lineaments. The host rocks and deep faults have the strongest spatial association with the known copper, lead, and zinc deposits. The hydrothermal alteration zones have the moderate spatial association with the copper, lead, and zinc deposits. The predicted high-favorability zones do not show the strong affinity with lineaments. The distribution of 22 (copper, lead, and zinc) occurrences in the Lanping Basin was examined in terms of spatial association with various geological phenomena. The analysis of these relationships using GIS and weights of evidence modeling has predicted areas of high and moderate mineral potential, where a little or no mining activities exist.

A comprehensive model for reproductive and developmental toxicity hazard identification: I. Development of a weight of evidence QSAR database

A weight of evidence (WOE) reproductive and developmental toxicology (reprotox) database was constructed that is suitable for quantitative structure–activity relationship (QSAR) modeling and human hazard identification of untested chemicals. The database was derived from multiple publicly available reprotox databases and consists of more than 10,000 individual rat, mouse, or rabbit reprotox tests linked to 2134 different organic chemical structures. The reprotox data were classified into seven general classes (male reproductive toxicity, female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity), and 90 specific categories as defined in the source reprotox databases. Each specific category contained over 500 chemicals, but the percentage of active chemicals was low, generally only 0.1–10%. The mathematical WOE model placed greater significance on confirmatory observations from repeat experiments, chemicals with multiple findings within a category, and the categorical relatedness of the findings. Using the weighted activity scores, statistical analyses were performed for specific data sets to identify clusters of categories that were correlated, containing similar profiles of active and inactive chemicals. The analysis revealed clusters of specific categories that contained chemicals that were active in two or more mammalian species (trans-species). Such chemicals are considered to have the highest potential risk to humans. In contrast, some specific categories exhibited only single species-specific activities. Results also showed that the rat and mouse were more susceptible to dysmorphogenesis than rabbits (6.1- and 3.6-fold, respectively).

Classification of a complex landscape using Dempster–Shafer theory of evidence

The landscape of the Highlands of Chiapas, southern Mexico, is covered by a highly complex mosaic of anthropogenic, natural and semi‐natural vegetation. This complexity challenges land cover classification based on remotely sensed data alone. Spectral signatures do not always provide the basis for an unambiguous separation of pixels into classes. Expert knowledge does, however, provide additional lines of evidence that can be employed to modify the belief that a pixel belongs to a certain coverage class. We used Dempster–Shafer (DS) weight of evidence modelling to incorporate this information into the classification process in a formal manner. Expert knowledge‐based variables were related to: (1) altitude, (2) slope, (3) distance to known human settlements and (4) landscape perceptions regarding dominance of vegetation types. The results showed an improvement of classification results compared with traditional classifiers (maximum likelihood) and context operators (modal filters), leading to better discrimination between categories and (i) a decrease in errors of omission and commission for almost all classes and (ii) a decrease in total error of around 7.5%. The DS approach led not only to a more accurate classification but also to a richer description of the inherent uncertainty surrounding it.

GIS-Based ( W+ — W−) Weight of Evidence Model and Its Application to Gold Resources Assessment in Abitibi, Canada

The weight of evidence (WofE) model has been widely used for mineral potential mapping. During the conversion of a multiclass map into a binary map a lot of mineralization information is artificially added or lost because the generalization of the class within the cumulative distance interval to a linear feature is based on a maximum contrast, which matches a cumulative distance interval. Additionally, some categorical data evidence cannot be generated by this method because a maximum contrast does not exist. In this article, an alternative (W+—W−)-based WofE model is proposed. In this model, the “(W+—W−) greater than zero or not” is used as a criterion to reclassify the corresponding categorical class into a presence or absence class to convert a multiclass map into a binary map. This model can be applied to both categorical data and successive data. The latter can be operated as categorical data. The W+ and W− of the generated binary maps can be recalculated, and several binary maps can be integrated on the condition that the reclassified binary evidences are conditionally independent of each other. This method effectively reduces artificial data and both nominal and categorical data can be operated. A case study of gold potential mapping in the Abitibi area, Ontario, Canada, shows that the gold potential map by the (W+—W−) model displays a smaller potential area but a higher posterior probability (POP), whereas the potential map by the traditional (W+—W−) model exhibits a larger potential area but a lower POP.

Predictive mapping for copper–gold magmatic-hydrothermal systems in NW Argentina: Use of a regional-scale GIS, application of an expert-guided data-driven approach, and comparison with results from a continental-scale GIS

Geographic Information Systems (GISs) are very useful tools for managing, checking, and organizing spatial information–from many sources and of many types–in thematic layers. Processing of these data enables exploration-oriented GISs to produce potential and predictive maps for a given commodity, which constitute documents of real use in decision-making. Integration of all information in a single reference system enables a better understanding of the parameters controlling a region's metallogeny, in terms of both time and space. But what scale should be used for developing a mineral exploration GIS? Should preference be given to systems with high spatial resolutions (scale < 1:500,000), or to more general systems with scales of around 1:1,500,000 or 1:2,000,000? Will the gain be worthwhile relative to the additional work generated by compilation at a higher scale? In order to make greater use of previous predictive studies performed on gold-rich epithermal and porphyry systems at the scale of the entire Andes, an expert-guided data-driven approach is now applied to a regional-scale GIS of NW Argentina, between the Puna and the Sierrras Pampeanas, where known deposits like Bajo de la Alumbrera, Agua Rica, and others, account for a metal potential of over 10 Mt Cu and 750 t Au. In developing this new predictive map, three criteria that were likely to be connected to the mineralizing event were selected and quantified: (i) lithostratigraphy, because of its role as a favourable environment for the development of mineralization, based on its physico-chemical properties; (ii) lithostratigraphic contacts, based on the rheological properties of the formations in contact; and (iii) the orientation of structural discontinuities, which channel source magmas and encourage the circulation of hydrothermal fluids. Assigning a score enables classification of the favourabilities calculated for each of the criteria considered. This approach is employed here to check and standardize the statistical results obtained by methods such as Weight of Evidence Modelling or an algebraic approach. For each criterion, four classes were distinguished: very favourable: score = 3; favourable: score = 2; slightly favourable: score = 1; and unfavourable: score = 0. The predictive map is obtained by adding the scores for the three favourable criteria defined above. The regional-scale work identified 20 anomalous envelopes with cumulative scores greater than 5. They correspond to mining areas that are active (e.g., Bajo de la Alumbrera), under development (e.g., Agua Rica), or abandoned (e.g., La Mejicana), or to new areas (e.g., the Vicuña Pampa Volcanic Complex). Structural analysis of the region, integrating the orientation of the favourable envelopes, suggests that the mineralizing fluids were emplaced under extensional conditions, sub-parallel to the principal directions of shortening: (i) WNW–ESE, found along the southern edge of the Puna; and (ii) E–W, seen in the Sierra de Famatina. It appears that a regional-scale information system is a tool that is well suited to the definition of areas for mineral prospecting, and to the study and confirmation of metallotects usable for mineral exploration. Comparison with work conducted on the basis of a 1:2,000,000 geological compilation shows that the principal mining districts can indeed be found at continental scale. On the other hand, the lack of detail inherent at a scale of 1:2,000,000 may lead to inaccuracies, in particular fictitious favourabilities assigned to formations that are genetically unrelated to the mineralization, but that contain, for example, small Tertiary intrusive bodies that cannot be recorded at this scale. This comparison therefore shows that the use of a continental-scale GIS is effective, and well suited to the definition of prospective areas at a strategic level.