Elsevier

Applied Geochemistry

Volume 132, September 2021, 105051
Applied Geochemistry

A copper porphyry promising zones mapping based on the exploratory data, multivariate geochemical analysis and GIS integration

https://doi.org/10.1016/j.apgeochem.2021.105051Get rights and content

Highlights

  • 1-prospectivity map generation of the promising zones for a copper porphyry mineralization.

  • 2-Using geological, geophysical and geochemical information.

  • 3-A multivariate geochemical analysis using PCA, Clustering and Discriminant analysis.

  • 4-Implementation of the PROMETHEE as one of the MADM method and modeling in GIS.

Abstract

The aim of this paper is to generate the mineral prospectivity mapping (MPM) for a porphyry copper mineralization area. First, three information layers of geochemistry, geophysics, and geology were defined as the main criteria with a total of 13 sub-criteria, eight for geochemical criterion, two for geophysical criterion, and three for geology criteria. As the Cu element is the main variable in copper porphyry mineralization. The geochemical criteria included the concentration values of 36 elements (Cu, Au, Mo, Pb, Zn, Ag, Ni, Co, Mn, Fe, As, Th, Sr, Cd, Sb, Bi, V, Ca, P, La, Cr, Mg, Ba, Ti, B, Al, Na, K, W, Hg, Tl, S, Sc, Se, Ga, and Te) from 87 rock samples that were analyzed by the induced coupled plasma (ICP) method. Before assigning the class scores for the quantitative and qualitative classes, a multivariate geochemical analysis (MGA) was done to reduce the number of the measured elements to a smaller number most related to the Cu mineralization. It was achieved through the probability plot modeling, calculating the threshold values of the Cu variable along with background and anomaly separation. The final step was discriminant analysis (DA), PCA, and agglomerative hierarchical clustering (AHC) analysis that reduced the number of elements to 8 elements as the geochemical sub-criteria. Then, the qualitative information was quantitated for the two criteria of geophysics and geology which were in the form of geophysical maps and geological information respectively. Then, the Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE) as one of the newest multiple attribute decision-making (MADM) methods was performed to obtain the net outranking flow for each criterion and for the integration of all criteria. Finally, the net outranking flow as the input data was used to generate the MPMs in Arc GIS 10.5 software which shows the promising areas for each criterion and for the integration of all criteria. The generated MPMs were then compared and the map obtained by integration of all criteria identified as a better result with the sharper boundary of the promising areas that can be used by decision-makers for future exploration programs.

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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