Application of data-driven multi-index overlay and BWM-MOORA MCDM methods in mineral prospectivity mapping of porphyry Cu mineralization

The present study applies proposed hybrid mathematical frameworks, namely Data-driven Multi-Index Overlay Method (DMIO) and Best Worst Method-multi objective optimization by ratio analysis (BWM-MOORA) techniques, for generating robust predictive models enhancing mineral prospectivity analysis is demonstrated. These are used for (i) weighting multiple groups of evidence maps acquired from geological databases, such as remote sensing, geological, geochemical, and geophysical, and then (ii) integrating these weighted layers into robust predictive models to assess porphyry copper prospectivity. This case study of the Chahargonbad area is a district of the volcano-plutonic Urumieh-Dokhtar magmatic arc belt. Porphyry copper deposit (exploration and development) models were used for distilling geoscientific datasets that are available into a group of evidence-based layers. The BWM-MOORA method combines two separate processes in which BWM is applied to weight each criterion and sub-criteria, whereas the MOORA approach helps rank these attributes. Each layer is first converted to another space of [0,1] by the sigmoidal logistic function to perform the critical process of integrating each of the evidence layers. Then, a concentration-area fractal model was used to classify evidences into some geospatial populations. In addition, a prospectivity / mineral potential mapping (MPM) analysis is performed using the DMIO, a well-proven, and accurate data-based methodology, to verify the satisfactoriness and validation of the applied methodology. Prediction-area (P-A) and normalized density (Nd) analysis (plots) were performed as validation methods to estimate the reliability and evaluate the relative efficiency of the MPM models. Therefore, these detailed outcomes confirm the validity of the BWM-MOORA model (Nd = 3.17), which is slightly higher than the valid and proper model DMIO (Nd = 3) in determining the specific potential mineralization regions.