Seismic Data Denoising With Correlation Feature Optimization Via S-Mean

Random noise elimination acts as an important role in the seismic data processing. Moreover, protecting and recovering useful subsurface structure information are also significant. In this study, the S-mean that can obtain the geometric mean of the seismic traces on the symmetric positive definite (SPD) matrix manifold is adopted as a nonlinear filter for seismic denoising. Furthermore, S-mean has the best correlation with other elements based on the S-divergence due to the optimization of finding the S-mean on the SPD manifold. Therefore, the broken correlation features in noisy seismic data are compensated and maintained well, which can be conducive to describe the subsurface structures. Synthetic examples and field data applications qualitatively and quantitatively demonstrate the validity and effectiveness of the proposed workflow.