Application of Multi-Source Data Mining Technology in the Optimization of Prospecting Target Areas for Copper Deposits in the Beishan Region of Gansu Province, China

The effectiveness of geological prospecting depends on the accuracy of the prediction of the prospecting target areas. In comparison with the conventional qualitative method (Mineral Exploration and Development), the use of big data concepts and methods for the in-depth analysis of the potential value of geological information has emerged as an effective way to improve the accuracy of prospecting target area predictions. The Beishan area in Gansu Province, China, is a prominent polymetallic metallogenic belt in northwest China. In recent years, geologists have encountered challenges in achieving effective breakthroughs in prospecting through conventional methods. In this study, we apply the big data concepts and methods to analyze the geochemical and aeromagnetic data of the Beishan area and utilize a series of self-developed software to rectify errors in the original data. A new geochemical remediation plan is proposed for the main elements of ore formation, and on this basis, a copper ore prospecting model based on multi-source data information mining is established. The prospecting model is used to predict the formation of copper ore in the Beishan area, and 100 level I and II preferred target areas with significant prospecting significance have been identified. Level I and II preferred target areas account for 2.7% of the study area. Verified by field sampling, the actual mineralization rate of the level I target area is 39.47%. This study proves the effectiveness of the proposed multi-source data mining method in improving the prediction accuracy of prospecting target areas.