Seismic Data Sparse Representation Using Swin Transformers

Abstract

Seismic data preprocessing significantly benefits from advanced sparse representation and domain transformation techniques to enhance denoising, wavefield separation, and data reconstruction. This study introduces a novel approach utilizing a deep learning framework for discrete sparse representation of seismic data. Our method utilizes a Swin Transformer-based encoding-decoding framework, which combines the hierarchical structures of CNNs with the self-attention mechanism of Transformers, to model both local and global information efficiently. This integration enables the precise characterization of seismic reflection events and the reconstruction of seismic records from a constructed sparse feature space. The proposed model has been rigorously tested on both simulated and field datasets, demonstrating its robustness, and potential provides superior decomposition of seismic data.