Local similarity-integrated self-learning model and its application to suppress seismic migration artifacts

ABSTRACT Seismic migration images with a high signal-to-noise ratio are crucial for oil and gas exploration and reservoir characterization. However, seismic images obtained with traditional migration methods often suffer from migration artifacts. Thus, one of the most important tasks for seismic data processing is properly separating and suppressing migration artifacts. Unlike random noise, migration artifacts exhibit a correlated distribution, which makes their separation and suppression particularly challenging. To address this issue, we suggest a local similarity-inspired self-learning model for suppressing them. This suggested model can automatically mine supervised information and train an effective denoising network with only the original noisy seismic data. The proposed method is mainly divided into two steps. The first step is to use the local self-similarity of seismic data to construct the training data. The second step is to adopt the prepared dataset generated in the first step to train the denoising model. The denoising network architecture mainly includes an encoder and a decoder. The former is used to extract the features of seismic data, and the latter is used to restore the effective information. After model training, we apply the suggested model to seismic data from two different volumes: the Penobscot dataset and a field dataset collected in the Ordos Basin in northwestern China. The results demonstrate that the suggested model can effectively separate and suppress seismic migration artifacts. Moreover, compared with the K-singular value decomposition and Noise2Sim, it could preserve valid signals better while attenuating migration artifacts more effectively.