Zero-Shot Self-Consistency Learning for Seismic Irregular Spatial Sampling Reconstruction
This addresses seismic exploration challenges by enabling stable reconstruction without extra data, though it appears incremental as it builds on existing zero-shot methods.
The paper tackled the problem of reconstructing irregularly sampled seismic data without requiring additional training datasets, achieving good reconstruction results and some noise suppression on the USGS National Petroleum Reserve-Alaska dataset.
Seismic exploration is currently the most important method for understanding subsurface structures. However, due to surface conditions, seismic receivers may not be uniformly distributed along the measurement line, making the entire exploration work difficult to carry out. Previous deep learning methods for reconstructing seismic data often relied on additional datasets for training. While some existing methods do not require extra data, they lack constraints on the reconstruction data, leading to unstable reconstruction performance. In this paper, we proposed a zero-shot self-consistency learning strategy and employed an extremely lightweight network for seismic data reconstruction. Our method does not require additional datasets and utilizes the correlations among different parts of the data to design a self-consistency learning loss function, driving a network with only 90,609 learnable parameters. We applied this method to experiments on the USGS National Petroleum Reserve-Alaska public dataset and the results indicate that our proposed approach achieved good reconstruction results. Additionally, our method also demonstrates a certain degree of noise suppression, which is highly beneficial for large and complex seismic exploration tasks.