As a product of hydrothermal activity, seafloor polymetallic sulfide deposit has become the focus of marine mineral exploration due to its great prospects for mineralization potential. The mineral prospectivity mapping is a multiple process that involves weighting and integrating evidential layers to further explore the potential target areas, which can be categorized into data-driven and knowledge-driven methods. This paper describes the application of fuzzy logic and fuzzy analytic hierarchy process (AHP) models to process the data of the Southwest Indian Ocean Mid-Ridge seafloor sulfide deposit and delineate prospect areas. Nine spatial evidential layers representing the controlling factors for the formation and occurrence of polymetallic sulfide deposit were extracted to establish a prospecting prediction model. Fuzzy logic and fuzzy AHP models combine expert experience and fuzzy sets to assign weights to each layer and integrate the evidence layers to generate prospectivity map. Based on prediction-area (P-A) model, the optimal gamma operator (γ) values were determined to be 0.95 and 0.90 for fuzzy logic and fuzzy AHP to synthesize the evidence layers. The concentration-area (C-A) fractal method was used to classify different levels of metallogenic probability by determining corresponding thresholds. Finally, Receiver Operating Characteristic (ROC) curves were applied to measure the performance of the two prospectivity models. The results show that the areas under the ROC curve of the fuzzy logic and the fuzzy AHP model are 0.813 and 0.887, respectively, indicating that prediction based on knowledge-driven methods can effectively predict the metallogenic favorable area in the study area, opening the door for future exploration of seafloor polymetallic sulfide deposits.
Pucheng district is a part of the Wuyi Mountain polymetallic metallogenic belt, which is constituted by Archean-Proterozoic metamorphic basements and Mesozoic volcanic-sedimentary covers. Uranium deposits are formed as volcanic-hosted and structural controls. In this study, the hybrid data-driven methods of logistic regression (LR) and weights of evidence (WofE) were applied for the mineral potential mapping of uranium in the Pucheng district. Evidential layers such as volcanic stratum, structure, igneous rock, alteration and radioactive anomaly were used in the mineral prospectivity analyses. The results show that the data-driven methods can not only measure the relative importance of each type of geological feature in uranium controls but also delineate prospective grounds for uranium exploration. The receiver operating characteristics (ROC) curve and under the ROC curve (AUC) were applied to measure the performance of the prospectivity models. The data-driven models are highly capable of mapping uranium prospectivity because AUC is close to 1. The results show that more than 90% of the known uranium deposits occur in regions with high probability. LR performs a little better than WofE in this area. The prospectivity mapping confirmed that there is significant potential for uranium mineralization for further exploration.
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