Application of the transfer learning method in multisource geophysical data fusion

Abstract

Using multigeophysical exploration techniques is a common way for deep targets to be explored in complex survey areas. How to locate an unknown underground target using multiple datasets is a great challenge. The useful information in the multisource geophysical model can be extracted and fused with the help of data fusion, which also works well to correct the interpretation divergence brought on by expert experience, with image feature extraction being the key step in the fusion of the geophysical models. Traditionally, this method is often used for these kinds of geophysical images, but it significantly reduces the efficiency of feature extraction. As a result, we propose a novel method based on a transfer learning method to extract the features of multisource images. First, the ResNet50 network is used to extract the initial features of the images. Owing to the problems of feature redundancy and fuzzy features in initial features, Spearman and zero phase component analysis can be used to achieve feature reduction and enhancement, which can further improve the computational efficiency and fusion accuracy in fusion. Finally, the fusion image is obtained using fusion rules that we designed based on the current state. The algorithm's reliability is tested using field data from the Iliamna Volcano. The case study demonstrates the effectiveness of the proposed strategy, which also offers a novel way to locate subsurface targets.