Abstract
This study presents an integrated approach for predicting the spatial distribution of gold, copper, and lithium concentrations using machine learning, geostatistical methods, and multivariate geospatial data. The problem is formulated as a spatially dependent multivariate regression task, distinguishing it from traditional classification-based mineral prospectivity approaches. A unified database was developed, incorporating geochemical indicators, geomorphometric terrain parameters, remote sensing data, and spatial coordinates. Correlation analysis with an adaptive threshold was applied to optimize the feature set and improve model robustness. The results show that linear methods are limited in capturing nonlinear relationships, while ensemble methods provide significantly higher predictive accuracy. In some cases, geostatistical methods achieve the best performance, emphasizing the importance of spatial structure. Feature importance analysis indicates that gold prediction is primarily driven by geochemical indicators, spatial coordinates, and terrain characteristics. Results for copper and lithium confirm the general applicability of the proposed approach. Overall, the study demonstrates the effectiveness of combining machine learning and geostatistics for modeling geochemical processes.