A Novel Training Strategy for the Interpolation of Consecutively Missing Case

Abstract

Self-supervised learning (SSL) reduces the dependence on labelled training data by exploiting the structure of unlabelled data. However, since SSL struggles with capturing high-level semantic information for complex tasks, it faces some difficulties in dealing with challenging tasks such as consecutively missing traces and weak signal recovery under strong noise. Although supervised learning can address these challenges, it demands high-quality training sets, which are often unavailable. To achieve a good balance between the performance and reliance on the training set, we propose a pseudo-self-supervision training strategy-based (PSL-based) by introducing the idea of SSL into the classical supervised framework and using the three-dimensional (3D) consecutive missing reconstruction to test its effectiveness. For incomplete seismic volumes, limited complete regions (sharing extremely similar properties as missing sections) can serve as some training data, yet they are typically too scarce to fulfil the requirements for effective training. Thus, we propose a 3D diffusion model for data augmentation and use the augmented data to construct paired training sets for network training. Since the proposed strategy still trains the network by learning the intrinsic features of the data themselves, we refer to it as the ‘PSL strategy’. Experimental results on both prestack and poststack field datasets demonstrate the effectiveness of the proposed PSL-based training strategy compared with SSL-based and supervised training strategies.