Exploration
Feridon Ghadimi; Arman Ghadimi
Abstract
The Geochemical Mineralization Probability Index (GMPI) represents a phase weight assigned to each geochemical stream sediment sample for individual indicator components. In this framework, the weights of the evidence map classes are determined based on the factor scores (FS) derived from factor analysis ...
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The Geochemical Mineralization Probability Index (GMPI) represents a phase weight assigned to each geochemical stream sediment sample for individual indicator components. In this framework, the weights of the evidence map classes are determined based on the factor scores (FS) derived from factor analysis for each indicator component. A wide range of direct and indirect approaches has been proposed to delineate prospective zones for mineral exploration; however, many of these methods are associated with substantial time and financial costs. Accordingly, this study aims to evaluate the performance of an Artificial Neural Network (ANN) model and to compare its predictive capability with that of conventional model-architecture-based approaches. In this research, a hybrid Artificial Neural Network–Biogeography-Based Optimization (ANN–BBO) model is developed to estimate the GMPI. The dataset used for model training and validation comprises geochemical concentrations of Au, Cu, Pb, Zn, Ag, Mo, W, and Sn obtained from 109 stream sediment samples collected in the Zaghar area. Biogeography-Based Optimization (BBO) is employed to optimize the ANN training process by adaptively adjusting its parameters to enhance predictive performance. The proposed ANN–BBO model achieved a Mean Squared Error (MSE) of 0.0221 and a coefficient of determination (R²) of 0.8244, indicating satisfactory predictive accuracy. Furthermore, the model demonstrated robust generalization capability, maintaining reliable predictive performance despite the considerable geochemical variability observed within the stream sediment dataset. The results of the sensitivity analysis reveal that Pb and Ag exert the most significant influence on the prediction of geochemical anomalies within the study area.
Exploration
Mobin Saremi; Saeed Yousefi; Mahyar Yousefi
Abstract
The Mineral Prospectivity Mapping (MPM) is a procedure of integrating various exploration data to identify promising areas for follow up mineral exploration programs. MPM facilitates identification of mineral deposit prospects through reducing search spaces for the purpose of mitigating cost and time ...
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The Mineral Prospectivity Mapping (MPM) is a procedure of integrating various exploration data to identify promising areas for follow up mineral exploration programs. MPM facilitates identification of mineral deposit prospects through reducing search spaces for the purpose of mitigating cost and time shortages. In this regard, geochemical anomaly maps constitute one of the most important evidential layers for MPM. In this research work, to produce an efficient geochemical evidential layer, the Staged Factor Analysis (SFA) method and Geochemical Mineralization Probability Index (GMPI) were performed on a dataset of 657 stream sediment samples. In addition to the mentioned maps, a layer of proximity to faults was used to efficiently identify the intended targets of copper hydrothermal deposits. The layers were then weighted and combined using logistic functions and the geometric average method. Based on the obtained results, the promising areas were found in three parts including western, central, and northern areas, which correspond to the faulted units of andesite, tuff, granite, and granodiorite intrusive masses. Finally, in order to evaluate the generated model, the prediction-area (P-A) plot was used, which shows the relative success of the generated map in specifying the desired exploration targets. The P-A plot showed that this model has a prediction rate of 64%. It seems that the proposed method by considering multi-element geochemical signatures and combination by another exploratory layer target the promising areas, those that are simultaneously present with other exploration evidence.
