Abstract
The difficulty of accurately inverting the self-potential (SP) source has always been a major factor hindering the wide application of the SP method. Considering that particle swarm optimization has poor accuracy when in the face of high-dimensional SP inversion, while the gradient method depends on the selection of the initial solution, we try to combine these two algorithms to prompt the inversion result to jump out of local optimum.
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