A Fuzzy Gold Mineral Prediction Model Integrating with Knowledge-Driven and Data-Driven: A Case Study of the Hatu Region in Xinjiang, China

Abstract

As mineral resources become increasingly scarce, the search for potential ore deposits presents a significant challenge in geological exploration. Subjective factors often constrain traditional knowledge-driven approaches, while purely data-driven methods may overlook the geological significance of data relationships, potentially compromising the accuracy and reliability of predictions. To address these issues, this study proposes a novel mineral prediction model that integrates fuzzy comprehensive evaluation with both knowledge-driven and data-driven approaches (FCEKDD), resulting in an optimized comprehensive mineral prediction model based on multi-source geoscience data. This model establishes comprehensive exploration indicators based on expert knowledge and quantitatively assesses these indicators through fuzzy evaluation methods to evaluate mineralization potential, thereby delineating exploration targets within the study area. Ultimately, a three-layer predictive framework is constructed using the C-A fractal method. Experimental results indicate that 57.1% of known mineral points are within the primary prediction zone, thus demonstrating the model’s high predictive accuracy. Comparisons with a random forest model reveal that the FCEKDD model has advantages in addressing geoscience data uncertainty and interpreting geological phenomena. This research validates the reliability and applicability of the proposed model in mineral exploration mapping, providing an effective solution for future mineral resource exploration.