Work

Adaptive SVD Denoising in Time Domain and Frequency Domain

Year: 2025

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app152212034

Authors: Meixuan Ren, Enli Zhang, Qiang Kang, Long Chen, Min Zhang, Lei Gao

Institutions: Exploration and Development Research Institute of PetroChina Southwest Oil & Gas Field Company (China), PetroChina Southwest Oil and Gas Field Company (China), Southwest Petroleum University

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: PetroChina Company Limited