A Novel Approach to Self-potential Data Interpretation in Support of Mineral Resource Development

Abstract

A new efficient technique has been developed to interpret self-potential data from different mineralized sources (horizontal cylinder, vertical cylinder, sphere, and 2-D inclined sheet). This technique is based on the first horizontal gradient filter and particle swarm optimization algorithm. This suggested method can be used for single-source and multiple-source interpretation. In this study, the developed technique was applied to five different synthetic examples (vertical cylinder model, horizontal cylinder model, sphere model, 2-D inclined sheet model, and multi-source model), using a real case from Canada—the multi-source field example—and two real cases from India. The results obtained from the synthetic and real data show that the method is fast, accurate, and effective in removing the regional background and does not require information regarding body shape. The results of the real data cases match well with the results obtained from other published methods.