Adaptive SVD Denoising in Time Domain and Frequency Domain
Source abstract
Abstract
In seismic data processing, noise not only affects velocity analysis and seismic migration, but also causes potential risks in post-stack processing because of the artifacts. The singular value decomposition (SVD) method based on the time domain and the frequency domain is effective for noise suppression, but it is very sensitive to singular value selection. This paper proposes a method of adaptive SVD denoising in both time and frequency domains (ASTF), 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 based 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: crossref · Quality: baseline
Research context
OpenAlex topics: Seismic Imaging and Inversion Techniques · Machine Fault Diagnosis Techniques · Seismic Waves and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Exploration and Development Research Institute, Southwest Oil and Gas Field, PetroChina, Chengdu 610041, China
School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China
Funding
Exploration and Development Research Institute, Southwest Oil and Gas Field, PetroChina
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