Adaptive SVD Denoising in Time Domain and Frequency Domain

In seismic data processing, noise not only affects velocity analysis and seismic migration, but also cause potential risks in post‐stack processing because of the artifacts. The singular value decomposition (SVD) method based on time domain and frequency domain is effective for noise suppression, but it is very sensitive to singular value selection. This paper proposes the adaptive SVD denoising in both time and frequency domains (ASTF) method with three steps. Firstly, two Hankel matrices are constructed in the time domain and frequency domain respectively. Secondly, the parameters of the reconstruction matrix are adaptively selected base on the singular value second‐order difference spectrum. Finally, the weights of these two matrices are learned through ternary search. Experiments were carried out on synthetic data and field data to prove the effectiveness of ASTF. The results show that this method can effectively suppress noise.