Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

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.

Estimation of Filtration Properties of Host Rocks in Sandstone-Type Uranium Deposits Using Machine Learning Methods

“…Classification of the covering surface near uranium ore processing zones for nuclear nonproliferation treaty compliance assessment using machine learning techniques and remotely sensed earth surface data was considered in [52]. In [53], a method for evaluating the prospectivity of tungsten deposits using machine learning and deep learning techniques. RF, SVC, ANN, and CNN were used to solve the classification problem in this study.…”

A Novel Approach for Resource Estimation of Highly Skewed Gold Using Machine Learning Algorithms

“…For example, artificial neural networks (ANNs) [6][7][8][9], adaptive neuro fuzzy inference system (ANFIS) [10], random forest (RF) [11], and Gaussian process (GP) [11,12], support vector machines (SVM) [13,14] k-nearest neighbors (kNN) [15], and combined kNN-ANN methods [2] are the most popularly used algorithms. There have also been some studies that employ machine learning and deep learning approaches to identify the mineral grade and potential anomalies, according to the following publications: [16][17][18]. So, MLAs are becoming more popular, especially in noisy data, such as the kind seen in vein deposits, because of their flexibility and capability to integrate nonlinear correlations between input and output data [19,20].…”

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

“…It is significant enough to construct accurate and efficient mineral prediction models and carry out quantitative mineral prospectivity mapping by data mining and artificial intelligence to exploit mineral resources. Data-driven models are commonly used in mineral prospectivity mapping, and specific mathematical models are used to quantitatively describe the statistics of potential evidence or spatial distribution to predict mineral targets [1][2][3][4]. With the rapid development of machine learning theory and technology, the toolset based on the data-driven models has been increasingly enriched.…”