Multi-Dimensional Data Fusion for Mineral Prospectivity Mapping (MPM) Using Fuzzy-AHP Decision-Making Method, Kodegan-Basiran Region, East Iran

Abstract

Analyzing and fusing information layers of exploratory parameters is a crucial stride for increasing the accuracy of pinpointing mineral potential zones in the reconnaissance stage of mineral exploration. Remote sensing, geophysical, geochemical, and geology data were analyzed and fused for identify metallic mineralization in the Kodegan-Basiran region (East Iran). Landsat 7 Enhanced Thematic Mapper Plus (ETM+), aeromagnetic data, geological data, and geochemical stream sediment samples were utilized. The study area contains some copper indices and mines. Thus, the main focus of this study was identifying the zones with high potential for metallic copper mineralization. A two-stage methodology was implemented in this study: First, extraction of the exploratory parameters related to metallic mineralization and second is data fusion by the hybrid fuzzy-analytic hierarchy process (Fuzzy-AHP) method. Hydrothermal alterations and iron oxides in the area were mapped by applying the optimum index factor (OIF), band ratio (BR), and least squared fit (LS-Fit) to ETM+ data. Intrusive masses were positioned as one of the effective parameters in identifying metallic mineralization zones using the gradient tensor method to assess aeromagnetic data. In order to determine the threshold concentration and the location of mineralization anomalies, the K-means clustering algorithm, vertical geochemical zonality (Vz) index, as well as concentration-area (C-A) multi fractal and singularity analysis were implemented on the geochemical data. In conclusion, the potential zones of metallic mineralization in the Kodegan-Basiran region were displayed in a mineral prospectivity map (MPM) derived from the Fuzzy-AHP decision-making method. Finally, to validate the prospectivity map of metallic mineralization, a control area was selected and surveyed by collecting mineralogical, petrological, and stream sediment samples. Field works confirmed the mineralization of Cu and Fe sulfides, oxides, and hydroxides. The high potential areas identified in the MPM can be considered as targets for future Cu exploration in the Kodegan-Basiran area.

An Adaptive Modeling-Based Aeromagnetic Maneuver Noise Suppression Method and Its Application in Mine Detection

“…Aeromagnetic detection is an effective method for finding metal deposits. In recent years, many scholars [1][2][3] have conducted research using aeromagnetic data for robust and low-cost identification of polymetallic minerals. Therefore, improving the quality of aeromagnetic data can lead to more effective mineral resource exploration.…”

Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran

“…It is definitely noteworthy that a combination of machine learning methods with geostatistical methods can optimize the geochemical modeling results [9]. For instance, it is essential to determine statistical communities when applying the linear discriminant analysis (LDA) algorithm [42,43]. Threshold limits of each community can be calculated with various methods, such as k-means clustering or concentration-area (C-A) fractal analysis.…”

Application of Analytic Hierarchy Process in Mineral Prospecting Prediction Based on an Integrated Geology-Aerogeophysics-Geochemistry Model

“…The exploration and verification results demonstrated the objectivity and effectiveness of this method. Ali et al applied the Fuzzy-Analytic Hierarchy Process method in eastern Iran and obtained comprehensive metal mineralization prospective areas [10][11][12][13][14][15][16].…”