Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain (original) (raw)
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Ore Geology Reviews, 2003
A data-driven application of the theory of evidential belief to map mineral potential is demonstrated with a redefinition of procedures to estimate evidential belief functions. The redefined estimates of evidential belief functions take into account not only the spatial relationship of an evidence with the target mineral deposit but also consider the relationships among the subsets of spatial evidences within a set of evidential data layer. Proximity of geological features to mineral deposits is translated into spatial evidence and evidential belief functions are estimated for the proposition that mineral deposits exist in a test area. The integrated maps of degrees of belief for the proposition that mineral deposits exist in a test area is classified into a binary mineral potential map. For the Baguio district (Philippines), the binary gold potential map delineates (a) about 74% of the training data (i.e., locations of large-scale gold deposits) and (b) about 64% of the validation data (i.e., locations of small-scale gold deposits). The results demonstrate the usefulness of a geologically constrained mineral potential mapping using data-driven evidential belief functions to guide further surficial exploration work in the search for yet undiscovered gold deposits in the Baguio district. The results also indicate the usefulness of evidential belief functions for mapping uncertainties in the geologically constrained integrated predictive model of gold potential. D
Natural Resources Research, 2014
Data-driven evidential belief (EB) modeling has already been demonstrated for mineral prospectivity mapping in areas with many (i.e., >20) deposits/prospects (i.e., with indicated/ inferred resources). In this paper, EB modeling is applied to a case-study area measuring about 920 km 2 with 12 known porphyry-Cu prospects and with evidential data layer containing missing values. Porphyry-Cu prospectivity of the same area has been modeled previously using weights-of-evidence modeling, which serves as reference for evaluating the results of EB modeling. Initially, EB modeling was used to quantify spatial associations of the known porphyry-Cu prospects with various geological features perceived to be porphyry-Cu mineralization controls. Spatial associations of the known porphyry-Cu prospects with geochemical data layers with missing values were also quantified. Then, geological and geochemical data layers found to have positive spatial associations with the known porphyry-Cu prospects were used as predictors of porphyry-Cu prospectivity. The results show that EB modeling is as efficient as WofE modeling in predictive modeling of mineral prospectivity in areas with as few as 12 prospects and with evidential data layers containing missing values.
Weights of evidence model is used to predict occurrence of an event with known evidences in a study area where training data are available to estimate the relative importance of each evidence by statistical methods. It provides a quantitative method for integrating multiple sources of evidences. It avoids subjective choice of evidences and subjective estimation of weights for evidences with comparing with other methods, such as Fuzzy logic method. This paper discusses our implementation of Weights of Evidence model as a data integration method in a GIS environment, and demonstrates working of the model using an application in the prediction of Sn-W-U mineral occurrence in the South Mountain Batholith in Nova Scotia, Canada. After introduction of the principal of weights of evidence model, the case study case is described, including data collection, data processing, and data integration with use of weights of evidence model. Finally, a Sn-W-U potential map with posterior probability values in the study area is generated and shown in this paper. Based on analysis of weights of five evidential maps and the final prediction result, it is concluded that the prediction result is successful and the model is useful in the study.
A Hybrid Fuzzy Weights-of-Evidence Model for Mineral Potential Mapping
Nonrenewable Resources, 2006
This paper describes a hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping that generates fuzzy predictor patterns based on (a) knowledge-based fuzzy membership values and (b) data-based conditional probabilities. The fuzzy membership values are calculated using a knowledge-driven logistic membership function, which provides a framework for treating systemic uncertainty and also facilitates the use of multiclass predictor maps in the modeling procedure. The fuzzy predictor patterns are combined using Bayes’ rule in a log-linear form (under an assumption of conditional independence) to update the prior probability of target deposit-type occurrence in every unique combination of predictor patterns. The hybrid fuzzy WofE model is applied to a regional-scale mapping of base-metal deposit potential in the south-central part of the Aravalli metallogenic province (western India). The output map of fuzzy posterior probabilities of base-metal deposit occurrence is classified subsequently to delineate zones with high-favorability, moderate favorability, and low-favorability for occurrence of base-metal deposits. An analysis of the favorability map indicates (a) significant improvement of probability of base-metal deposit occurrence in the high-favorability and moderate-favorability zones and (b) significant deterioration of probability of base-metal deposit occurrence in the low-favorability zones. The results demonstrate usefulness of the hybrid fuzzy WofE model in representation and in integration of evidential features to map relative potential for mineral deposit occurrence.
Weight of Evidence Allocates Mineral Depositional Zones
American Journal of Environmental Sciences, 2010
Problem statement: Yunnan province in general and Pulang area in particular is geological rich area which prevents field study in multi locations due to high risk outcrop. Approach: New technology such as Geographic Information System (GIS) and an ArcView extension module Arcweight 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 the purpose of rapid evaluation of mineral resources a serial of modeled prediction methods were established. Weight of evidence model is to predict the existent thing by combining the known evidence of the study area, the importance of evidence is determined based on statistical method. Contrary to the fuzzy logic method, it avoids the subjective 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 the entire study area. Predicted results show clearly that the boundary of potential areas and the non-potential areas is clear. Therefore, fuzzy logic and other methods should be applied to predict the results for further comparison. More accurate prediction would draw a big smile on faces of share holders.
Arabian Journal of Geosciences, 2016
The development in the emerging technologies of information and communications requires more rare metals. The existing resources, insufficient to assume this progress, require further investigations to discover new rare metal deposits. The traditional methods, based on manual overlay, are unsuitable and expensive. Thus, mineral exploration requires updated methods to easily, quickly, and cost effectively delineate new promising exploration zones. Geographical Information System (GIS) and applied geomatics provide and perfect various modeling techniques implemented in GIS software. In recent years, two spatial modeling techniques were developed and widely applied in mineral exploration, data-driven methods, and knowledge methods. Weight of evidence (WofE) is a data-driven method based on the Bayesian theorem and its fundamental concept of prior and posterior probabilities. The method combines statistically diverse geodata that represent ore-controlling factors by weighting their evidence using "control points" to create a "posterior probability map." Our study area, located at the southern part of Hoggar in the south of Algeria, is potential for Sn, W, and rare metals and encloses several deposits related to peraluminous post-orogenic rare metal granitoïds (RMGs). In this work, "weights of evidence" modeling is applied to map mineral potential of this style of mineralization. Seventeen predictor maps, representing the deposit recognition criterion model, were generated from multi-source geodata (lithology, geochemistry, tectonic, magmatism, and geophysics). These data were used as "input data" and the known deposits (48 mineral occurrences) as "training sites." The WofE modeling gets the following results: (1) generate an output map called "mineral potential map" (MPM), where potential zones are reduced to small areas; (2) the MPM efficiently predicts the well-known deposits of Nahda, Sedis, Rechla, and Tit N'Enir; and (3) highlights some unrecognized areas such as Tedjrine, Monts de Tessalit, and Gara Akeboum. (4) The control model demonstrates the possibility to extend the WofE method to the adjacent regions enclosing a small number of known mineral deposits.
Formalization and Estimation of Integrated Geological Investigations: An Informational Approach
GEOINFORMATICS, 2003
All geological investigations are used in the definite succession in the time and space. For this different geological means are employed (mining works, drilling, geophysical and geochemical investigations, etc). The final expected aim of geological prospecting is the best recognition of studied area by given limitations. Effectiveness of geological mean application is based on three factors: cost, time and informational criterions. Cost and time criterions maybe estimated by simple calculation, but determination of last criterion is a complicated problem. Informational criterion model is composing from three factors: (1) quantitative estimation of information, (2) estimation of informational reliability and (3) estimation of informational value by degree of aim achievement according to the pragmatic criterion. The main aim of the paper is a problem of determination of set of means composing the notion "geological prospecting" (relative to some fixed feature) by assumed reliabilities of the means. The reliability of geological prospecting means is considered at the level of local determination. Reliabilities of information obtaining by separate mean and set of means are analyzed in detail. Suggested procedure of determining reliability for means and sets of means relative to feature is based on improved methodology of conditional probability utilization. The ways providing the increment of reliability of geological means are proposed. The applicability of proposed methods is shown on simplified examples. Utilization of the methods will allow to finding the most optimal combinations of geological means in different physical-geological conditions. Introduction This algorithm is based on the fundamental terms of information theory and combined with the structural (hierarchical) approach. This approach allows to constructing each geological indicator as a structure reflected a set of typical situations. After this the depth of searching recognition is estimated and calculated using developed informational approach. Realization of proposed strategy provides quantitative calculation and effective control of geological/environmental studies. In real conditions many random factors disturb the results obtained by a set of geological means. One of the essential problems consists of impossibility to obtaining satisfactory formalized description of factors influencing to results of local determinations. The similar situations are known in the theory of "decision making" where a full math formalization of the investigated problem is complicated. Our experience (Borisovich and Eppelbaum et al., 2001) allows us to suggesting that application of expert methods in many situations (set of logical and math-statistical procedures) will be most effective.
Natural Resources Research, 2015
Index overlay and Boolean logic are two techniques customarily applied for knowledgedriven 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.
Natural Resources Research, 2000
Weights of evidence (WofE) modeling usually is applied to map mineral potential in areas with large number of deposits/prospects. In this paper, WofE modeling is applied to a case study area measuring about 920 km 2 with 12 known porphyry copper prospects. A pixel size of 100 m × 100 m was used in the spatial data analyses to represent in a raster-based GIS lateral extents of prospects and of geological features considered as spatial evidence. Predictor maps were created based on (a) estimates of studentized values of positive spatial association between prospects and spatial evidence; (b) proportion of number of prospects in zones where spatial evidence is present; and (c) geological interpretations of positive spatial association between prospects and spatial evidence. Uncertainty because of missing geochemical evidence is shown to have an influence on tests of assumption of conditional independence (CI) among predictor maps with respect to prospects. For the final predictive model, assumption of CI is rejected based on omnibus test but is accepted based on a new omnibus test. The final predictive model, which delineates 30% of study area as zones with potential for porphyry copper, has 83% success rate and 73% prediction rate. The results demonstrate plausibility of WofE modeling of mineral potential in large areas with small number of mineral prospects.
Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping
Nonrenewable Resources, 2003
In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the “model” and “validation” base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.