Risk-Aware Quantitative Mineral Prospectivity Mapping with Quantile-based Regression Models

In the realm of deep resource exploration, risk is a factor that cannot be neglected. This study innovated existing quantitative mineralization prediction models with consideration of risk. Different from conventional approaches, which primarily focus on the conditional means and show obvious limitations in handling enriched or barren mineralization that deviate significantly from the mean, quantile regression (QR), as a method to predict the conditional distribution instead of conditional means, is expected to break through these limitations and to be used further for risk prediction. Drawing upon data from the Xiadian gold deposit, five geological factors were extracted as explanatory variables and gold grade was used as response variable. Four QR-based regression models were employed to predict the conditional distributions of gold grade. The comprehensive performance evaluation and comparison of these models focus on reliability, clarity, and their combination. The results unequivocally indicate that the quantile regression forest (QRF) model outperformed the other QR-based prediction models. Subsequently, a detailed performance analysis was conducted on the optimal QRF model, followed by a comparison with the RF model to validate its effectiveness. Building upon this foundation, by analyzing the predictive results of QRF models at certain quantiles in unknown regions, several discernible targets were delineated at different risk levels. Overall, this paper introduces the consideration of risk in mineral prospectivity prediction and attempts to predict the conditional distribution of mineralization in deep mineral prospectivity mapping contexts. These insights can offer valuable guidance in identifying optimal targets and in reducing exploration risks.