Complex Noise Suppression Using a Robust Dictionary Learning Approach

Seismic data comprise reflection signals and various types of noise, including random high-frequency ambient noise, high-amplitude noise, and low-frequency ground-roll noise. Noise removal while protecting useful signals has been the long-term research focus of seismic exploration and data processing. Seldom methods can be used to deal with all these types of noise simultaneously, which motivates the study of this research. Here, we propose a systematic and effective method to tackle various kinds of complex seismic noise in seismic data by designing a dictionary learning approach. To deal with random and high-amplitude noise, we propose a robust inversion framework to estimate the signals from noise-corrupted data iteratively. The inversion scheme is inspired by an L1 -norm regularized iterative denoising framework but performs conveniently in terms of the coherency-promoting denoising operator. Then, we propose a bandpass-limited signal retrieving scheme internally within the iterative framework to tackle the ground-roll suppression issue. The iterative robust dictionary learning approach is based on a singular-value decomposition (SVD)-free approach to improve the efficiency without sacrificing the denoising performance. Results from synthetic and real data examples show promising performance of the proposed method.