Locating the source of self-potential using few-shot learning

Abstract

Self-potential method has the advantages of convenience, rapidity and sensitivity. It has many applications in geophysical exploration. Therefore, the inversion problem of self-potential is very important. In order to obtain the location of the source, we propose a new self-potential inversion method based on few-shot learning. Firstly, a fully-connected neural network is used to extract the features of self-potential data and the data of well-surface pole–pole device method, and then the cosine distance is used to calculate the similarity between them. So, we can transform the positioning problem on the two-dimensional (2D) profile into the classification problem in two directions. In order to verify the feasibility of this method, we carried out a simulated landfill leakage experiment and a field saline water diffusion experiment, and both achieved good results. Compared with self-potential tomography method, the positioning accuracy of our method is increased by about 10.42%. Compared with traditional deep learning methods, we combine multimodal data to make the results more interpretable. In addition, the results of the two experiments also prove that our method has strong generalization ability, which provides a new idea to solve the differences between the simulation model and the actual field.