Metallogenic prediction based on fractal theory and machine learning in Duobaoshan Area, Heilongjiang Province

Abstract

The extraction and integrated analysis of multi-source geological data are key steps in the prediction of mineralization. Current studies are focusing on the extraction and integration of the deep-level mineralization information. In the era of big data, mathematical models and computer algorithms for data mining of multi-source prospecting information have emerged as a leading research area in mineral prediction. In this study, we quantitatively analyzed the structure and remote sensing alteration information using the concentration–area (C–A) fractal model and the box-counting method for the Duobaoshan mineralization area, Heilongjiang Province, China. Results indicate that areas of high fractal dimension of remote sensing alteration correspond to abundant alteration anomalies. Fractal characterization of geological structure is consistent with the spatial distribution. Therefore, fractal characterization provides predictive factors of structure and remote sensing alteration in the development of a predictive model of mineralization. Soil geochemical data were analyzed using the component data analysis (CDA) method and the spectrum–area (S–A) fractal model. The analyses identified anomalous and background signals represented by the PC1 and PC2 principal component combinations. These combinations show a strong correlation between geochemical anomaly data and known deposits in the study area, suggesting that the S–A model effectively identifies geochemical anomalies that can be used as a predictive factor of a mineralization prediction model. The mineralization prediction model was developed using random forest (RF) and support vector machine (SVM) algorithms. The model incorporates predictive factors from multiple sources, including the ore-forming geological background, fractal-characterized geological structure, fractal-characterized remote sensing alteration, and geochemical characteristics. The models incorporated the C–A fractal model to evaluate the probability of mineral prediction. By integrating the characteristics of multi-source mineral prospecting information with the predictive results of machine-learning models, we delineated eight prospective mineralization areas. This approach validates the effectiveness of a combined method involving fractal theory and machine-learning in mineral exploration, offering new insights and theoretical guidance for further mineral prospecting in the study area.