Combining numerical modeling and machine learning to predict mineral prospectivity: A case study from the Fankou Pb-Zn deposit, southern China

Predictive modeling of mineral prospectivity, which integrates diverse evidence of ore-forming processes based on an ore deposit genetic model constitutes a significant task in mineral exploration targeting. Descriptive de-posit models suffer from overwhelming dependence on the explicitly mappable characteristics of a given deposit used to simplify the complex mineral system, but this hinders mineral prospectivity modeling. This study pro-poses a hybrid approach that combines numerical modeling with machine learning to predict mineral prospectivity. This approach first uses the numerical modeling method to provide more insights and better characterize a complex mineral system. These comprise implicit footprints such as singular stress deformation, heat transmission and fluid flux inherent in the transient geodynamics of ore genesis. The approach then applies a machine learning algorithm as a data-fusion tool to integrate the simulated evidence to minimize uncertainty in the predictive modeling of mineral prospectivity, which fundamentally results from an incomplete understanding of the targeted mineral system. Finally, we use the Fankou Mississippi Valley type Pb-Zn deposit as an example to illustrate the application of the proposed hybrid method. The prospectivity modeling result is critically compared with that of the spatial modeling based on descriptive deposit modeling by means of measures of accuracy, area under the receiver operating characteristic curve, and normalized density of prediction-area plot. It is shown that the overall performance of the prospectivity map predicted using a hybrid method is significantly improved. As such, this study demonstrates that a hybrid framework combining numerical modeling with ma -chine learning provides a useful and novel way of predicting mineral prospectivity. The proposed hybrid method embodies a pioneering paradigm that integrates physics and data, assimilating ore deposit genesis models and reconnaissance data for the prediction of mineral prospectivity in support of exploration targeting. This approach can be implemented to enhance exploration information systems through the predictive modeling of mineral prospectivity in a unified computing and decision-making environment.