Seismic CRP Gather Coherent Noise Suppression via Neural Network Low-Rank Approximation

Coherent noise suppression in Common Reflection Point (CRP) gather is a crucial task in seismic data processing. The valid signals in CRP gathers are horizontally aligned which can be represented effectively by low-rank approaches but not the coherent noise. Leveraging this characteristic, we propose a neural network low rank approximation method for CRP gather coherent noise suppression. Specifically, to increase the self-similarity between adjacent traces in CRP gather, we first use plane-wave structural prediction operator to flatten seismic events within a local neighboring window. Subsequently, a fully connected neural network with low rank regularization is used to approximate the locally flattened seismic data. Moreover, a transfer learning strategy is employed to improve the efficiency of multiple seismic gather processing.blueOn two field CRP gathers, the proposed method achieves the lowest local similarity (LS) values of 0.0328 and 0.0629, compared with both state-of-the-art (SOTA) and traditional methods. The denoising results on both synthetic and field data demonstrate the effectiveness of the proposed method both in attenuating coherent noise and protecting valid signals.