Seismic data denoising using a new framework of FABEMD-based dictionary learning

Land seismic data are often obscured by noise, severely affecting the accuracy of subsequent seismic imaging and interpretation. Dictionary learning (DL) is an effective method for noise suppression. However, finding a fast DL method that is suitable for weak signals and can suppress multi-scale strong noise is still a hot topic. In this paper, we introduce a noise suppression method that combines DL with fast adaptive empirical mode decomposition (FABEMD). We leverage the advantages of FABEMD in multi-scale signal decomposition, along with the efficient sparse representation capabilities of DL, to achieve noise suppression for low signal-to-noise ratio seismic signals. We group bi-dimensional intrinsic mode functions based on their cross-correlation coefficients and train dictionaries for components using the sequential generalization K-means method, enhancing computational efficiency and adaptability. Numerical examples using both synthetic and field data validate the practicality and versatility of the proposed method, indicating its improved performance in denoising compared to f-x EMD, BEMD, and traditional DL methods.