Abstract
Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and a deep learning convolutional neural network (CNN), were employed to conduct a data-driven W prospectivity modelling of the southern Jiangxi Province, China. A total of 118 known W occurrences derived from long-term exploration of this brownfield area and eight evidential layers of multi-source geoscience information related to W mineralization constituted the input datasets. This provided a data-rich foundation for training machine learning models. The optimal configuration of model parameters was trained by a grid search procedure and validated by 10-fold cross-validation. The resulting predictive models were comprehensively assessed by a confusion matrix, receiver operating characteristic curve, and success-rate curve. The modelling results indicate that the CNN model achieves the best classification performance with an accuracy of 92.38%, followed by the RF model (87.62%). In contrast, the RF model outperforms the rest of ML models in overall predictive performance and predictive efficiency. This is characterized by the highest value of area under the curve and the steepest slope of success-rate curve. The RF model was chosen as the optimal model for mineral prospectivity in this region as it is the best predictor. The prospective zones delineated by the prospectivity map occupy 9% of the study area and capture 66.95% of the known mineral occurrences. The geological interpretation of the model reveals that previously neglected Mn anomalies are significant indicators. This implies that enrichment of ore-forming material in the host rocks may play an important role in the formation process of wolframite and can represent an innovative exploration criterion for further exploration in this area.
Highlights
Mineral prospectivity modelling (MPM), known as mineral prospectivity mapping, aims to outline and prioritize prospective areas for exploring undiscovered mineral deposits of a particular type [1,2]
Two primary contributions of this work can be summarized below: First, to the best of our knowledge, this paper reports regional prospectivity modelling in this important ore district for the first time, with application and comparison of various machine learning and deep learning algorithms based on integration of multi-source explorative information
The support vector machine (SVM) models produce poor classification results with low cost value (
Summary
Mineral prospectivity modelling (MPM), known as mineral prospectivity mapping, aims to outline and prioritize prospective areas for exploring undiscovered mineral deposits of a particular type [1,2]. Prospectivity modelling is a process of establishing an integration function relating a series of geological features (input variables) to the presence of the targeted mineral deposits (output variables) [3]. This process involves two key steps: (i) Generation of reasonable evidential maps that represent spatial proxies of the ore-forming processes based on the conceptual model of the deposit-type sought and available multi-source exploration dataset (e.g., geological, geophysical, geochemical, and remoting sensing data) [4], and (ii) integrating and weighting evidential maps using diverse mathematical algorithms to create a function that approximate the occurrence of mineral deposits. Knowledge-driven MPM models are appropriate for under-explored (or greenfield) areas, where MPM is conducted to delineate new exploration targets
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