Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects,Abra, Philippines

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 km² 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.     

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

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

Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.     

Physico-chemical seasonal variability of a tropical estuary: major and minor elements in water and air

The lower reaches of the Coatzacoalcos River in southeast Mexico is an area of intense industrial development. The physico-chemical characteristics of the area have exhibited differences over the years. Apparently from the associated outcroppings of limestone in the Uxpanapa River Basin, the major elements that are dissolved show higher concentrations of Ca, Mg and HCO₃^– in the waters supplied by this river. The water in the Calzadas River contains high concentrations of Ca, SO₄ and HCO₃^– that are associated with the saline domes crossed by this river. Due to industrial discharges, the sulfate concentration is very high in the water and air during April. Nitrate concentration diminishes with salinity. Higher nitrate as well as nitrite and ammonia levels are present during flood season. Phosphate concentration, associated with high oxygen levels, is higher in January. Zn, Cu and Cr are higher during the dry season (April) when dilution is minimal and low levels of TOC are present. The smaller concentrations of Zn and Cu observed in January are associated with high TOC values in water. The lower levels of Cr present in August are associated with high amounts of suspended matter. Pajaritos Lagoon and Teapa-L, with large industrial discharges, have the highest nutrient and dissolved metal concentrations in the area. Air particles smaller than 2.5 m contain Fe, V, Ti, Cu, Zn, and high amounts of S. These anomalous concentrations of sulfates and metals are attributed to anthropogenic sources.     

Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping

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.     

Representation of Uncertainty and Integration of PGIS-based Grazing Intensity Maps Using Evidential Belief Functions

In a project to classify livestock grazing intensity using participatory geographic information systems (PGIS), we encountered the problem of how to synthesize PGIS-based maps of livestock grazing intensity that were prepared separately by local experts. We investigated the utility of evidential belief functions (EBFs) and Dempster's rule of combination to represent classification uncertainty and integrate the PGIS-based grazing intensity maps. These maps were used as individual sets of evidence in the application of EBFs to evaluate the proposition that " This area or pixel belongs to the high, medium, or low grazing intensity class because the local expert(s) says (say) so ". The class-area-weighted averages of EBFs based on each of the PGIS-based maps show that the lowest degree of classification uncertainty is associated with maps in which "vegetation species" was used as the mapping criterion. This criterion, together with local landscape attributes of livestock use may be considered as an appropriate standard measure for grazing intensity. The maps of integrated EBFs of grazing intensity show that classification uncertainty is high when the local experts apply at least two mapping criteria together. This study demonstrates the usefulness of EBFs to represent classification uncertainty and the possibility to use the EBF values in identifying and using criteria for PGIS-based mapping of livestock grazing intensity.     

Selection of Less Biased Threshold Angles for SAM Classification Using the Real Value–Area Fractal Technique

The accuracy of classification of the Spectral Angle Mapping (SAM) is warranted by choosing the appropriate threshold angles, which are normally defined by the user. Trial‐and‐error and statistical methods are commonly applied to determine threshold angles. In this paper, we discuss a real value–area (RV–A) technique based on the established concentration–area (C–A) fractal model to determine less biased threshold angles for SAM classification of multispectral images. Short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images were used over and around the Sar Cheshmeh porphyry Cu deposit and Seridune porphyry Cu prospect. Reference spectra from the known hydrothermal alteration zones in each study area were chosen for producing respective rule images. Segmentation of each rule image resulted in a RV–A curve. Hydrothermal alteration mapping based on threshold values of each RV–A curve showed that the first break in each curve is practical for selection of optimum threshold angles. The hydrothermal alteration maps of the study areas were evaluated by field and laboratory studies including X–ray diffraction analysis, spectral analysis, and thin section study of rock samples. The accuracy of the SAM classification was evaluated by using an error matrix. Overall accuracies of 80.62% and 75.45% were acquired in the Sar Cheshmeh and Seridune areas, respectively. We also used different threshold angles obtained by some statistical techniques to evaluate the efficiency of the proposed RV–A technique. Threshold angles provided by statistical techniques could not enhance the hydrothermal alteration zones around the known deposits, as good as threshold angles obtained by the RV–A technique. Since no arbitrary parameter is defined by the user in the application of the RV‐A technique, its application prevents introduction of human bias to the selection of optimum threshold angle for SAM classification.     

Modeling of the Correlation Between Mineral Size and Shale Pore Structure at Meso- and Macroscales

Mathematical Geosciences - Inorganic pore structures are critical to understand the oil and gas transport and storage properties of unconventional reservoirs. However, it can be difficult to...