A copper porphyry promising zones mapping based on the exploratory data, multivariate geochemical analysis and GIS integration
Introduction
Geochemical exploration is an essential component in describing the natural environment. The application extent of these surveys varies from continental environmental to regional and detailed works in mineral exploration. Actually, it is an application of geochemical science that aims to indicate the occurrence of the mineral deposits. Geochemical exploration measures one or more chemical properties of natural materials, such as rock, soil, stream sediments, surface or groundwater, vegetation, dust, and gases, with the main objective of identifying and locating the presence of anomaly (abnormality) in the concentrations of chemical elements. The most common parameters are concentrations of chemical elements or compounds (Licht, 1998).
Combined anomalies may be more robust or more indicative than a particular type of anomaly for the individual elements. In prospecting, geochemistry works with many variables simultaneously, because it is the set of variables that models a geochemical landscape, not just one. As the various variables interact to form the final observed picture, sometimes some of these interactions and associations appear clearly in multivariate studies. One of the functions of the multivariate analysis is to reduce the size of the data, the results of which are presented in two or three-dimensional (Landim, 2011; Moradpouri and Ghavami-Riabi, 2020, Liu et al., 2020; Grunsky and de Caritat, 2020).
Geochemical analyzes are conducted for different purposes including multivariate geochemical analysis (MGA), background and anomaly separation, geochemical halos studies, zoning index, erosion surface determination, and many other purposes. The final results of a geochemical analysis might be mineral prospectivity mapping (MPM) that addresses the promising targets for future investigations (Pan and Harris, 2000; Porwal et al., 2003). The MPM has been generated using the two methods of data and knowledge driven for well explored and less-explored areas respectively (Bonham-Carter, 1994; Caranza, 2008). Some researchers used the data-driven approaches such as weight of evidence, logistic regression, and neural network for analysis and mapping the anomalous areas (Porwal et al., 2003; Kreuzer et al., 2010; Caranza, 2011; Porwal; Caranza, 2015; Sun et al., 2019). On the other hand, some used the knowledge-driven approaches such as fuzzy logic and Boolean to generate the MPM (Bonham-Carter, 1994; Nykänen and Ojala, 2007; Caranza, 2008).
Also, the MPM could be generated through the methods of multiple attribute decision-making (MADM). MADM methods in principle seek relationships of preferences subjective among alternatives influenced by various criteria (Wiecek et al., 2008). Many researchers have been presented the MPM based on the different methods of MADM. Behara et al. (2019) used the fuzzy AHP methods and fractal concepts to map the geological anomaly of gold and mineralization in India. Du et al. (2016) described a GIS-based fuzzy AHP for the promising porphyry copper mineralization areas. The PROMETHEE method (Preference Ranking Organization Method for Enrichment Evaluations) is one of the most recent MADM methods in the family of partial (PROMETHEE I) and complete (PROMETHEE II) outranking methods. Brans (1982) improved this method and it was continued by Vincke and Brans (1985). In these methods, the result of the comparison between any two alternatives is expressed in terms of a preference function that must reflect each criterion.
The aim of this paper is to generate the MPM for a porphyry copper mineralization property using the geochemical, geophysical, and geological layers as the main criteria with a total of 13 sub-criteria. Before the PROMETHEE implementation, the MGA including the probability plot modeling for background and anomaly separation, and thresholds values calculation was carried out. Then, discriminant analysis (DA), principle component analysis (PCA), and an agglomerative hierarchical clustering (AHC) were done to determine the main elements (variables) related to the Cu mineralization as the quantitative geochemical criteria. Finally, the PROMETHEE procedure was implemented to obtain the net outranking flow for generating the MPM in Arc GIS 10.5 software.
Section snippets
Porphyry type deposits
Porphyry deposits are a kind of metallic mineralization type related to the magmatic-hydrothermal transport of metal along with fractures from depth to near-surface. Porphyry type deposits are generally recognized from the presence of (a) veins and venules (forming stockworks), (b) mineralization is spatially and genetically associated with intrusive bodies with distinct porphyritic texture, (c) large volumes of rock are affected by alteration mineralization of hydrothermal origin. Hydrothermal
Data and criteria description
A collection of three main criteria including geochemical, geophysical and geology information was used for PROMETHEE implementation. The geophysical criterion included the magnetic and induced polarization (IP) anomalies obtained from the available geophysical maps and the geology criterion includes the sub-criteria of heat source, host rock, and alteration. Also, the sub-criteria of geochemical criterion included the elements concentration values for 87 rock samples which were analyzed by ICP
Multivariate geochemical analysis
The aim of the multivariate geochemical analysis (MGA) is to identify the relationships in the data as well as any necessary processing, modeling, and interpretation. First, the concentration value of the Cu element (variable) was analyzed for the statistical parameters, histogram, and normality test. Then the probability plot modeling was used for the background and anomaly separation which form the basis of the discriminant function method (DFM). The Cu element is also the main variable for
PROMETHEE procedure
Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE) is one of the MADM methods that have been found as an efficient decision-making tool which provides complete ranking order of all available alternatives prudently, thus avoiding errors in decision making. It use qualitative and quantitative criteria, the ordered fashion of the decision making which allows a good traceability of the decision and the quality assurance given by the consistency indices (Tscheikner-Gratl et
GIS integration and prospectivity mapping results
Although the multi-attribute decision-making analysis (MADM) has been widely used in decision making related to problems with different criteria and sub-criteria, it is a concern that this information forms a single rating index to be properly used by the decision-makers to make a right, easy and fast decision. On the other hand, Geographic Information Systems (GIS) is a computational tool for querying, analyzing, editing and mapping spatial information using spatial database. In digital maps,
Conclusion
As the different phases of a mineral exploration project is often done step by step and not in the parallel procedure. The time and costs as well as the continuation or cessation of the project depend on the results of each step which may take months or even years. In some cases, despite performing several steps, it is not easy to limit the initial area to a smaller promising area to continue the exploration work. In fact, more information should make it easier for the experts to make
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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