Enhancing Prospecting Target Prediction Precision: A Multi-Source Data Mining Approach in Gansu’s Beishan Area

The success of geological prospecting depends on the accuracy of target area prediction. Traditional qualitative research methods rooted in theoretical frameworks have shown significant limitations, especially in their inability to fully exploit the latent value of existing geological information. Applying big data concepts and methodologies to geological information mining has emerged as an effective way to improve the accuracy of prospecting target prediction. This study is founded on the core principle of geoscience big data: to “uncover correlations within data to address geological issues”. Taking geochemical prospecting and aeromagnetic data from the Beishan area in Gansu Province as a case in point, this study emphasizes the significance of meticulous data processing in averting potential errors. A suite of prospecting models was developed through multi-source data mining to identify potential gold deposits. Notably, aeromagnetic data were innovatively employed for the first time to predict the occurrence of non-magnetic minerals, which are primarily structurally altered rock-type and quartz vein-type gold deposits. The developed prospecting model was used to predict metallogenesis in the Beishan area of Gansu Province. The prospecting target area was delineated, accounting for 3.67% of the study area. Verification using field sampling data revealed that the actual mineralization rate in the level-I target area reached 52.6%. The research results suggest that this approach can substantially enhance the accuracy of prospecting target area prediction.