Sparse Time-frequency Analysis of Seismic Data: Sparse Representation to Unrolled Optimization

Time-frequency analysis (TFA) is widely used to describe local time-frequency (TF) features of seismic data. Among the commonly used TFA tools, sparse TFA (STFA) is an excellent one, which can obtain a TF spectrum with good readability. However, many STFA algorithms suffer from expensive calculation...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 61; p. 1
Main Authors: Liu, Naihao, Lei, Youbo, Liu, Rongchang, Yang, Yang, Wei, Tao, Gao, Jinghuai
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Analysis
Approximation
Approximation algorithms
Constraint modelling
Deep learning
Fourier transforms
Frequency analysis
Frequency dependence
Generators
Iterative methods
Mathematical models
Modules
Optimization
Readability
Reconstruction
Seismic attenuation
Seismic data
Signal processing
Signal processing algorithms
Sparse time-frequency analysis
Time-frequency analysis
Transforms
Unrolled algorithm
Unsupervised learning
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Time-frequency analysis (TFA) is widely used to describe local time-frequency (TF) features of seismic data. Among the commonly used TFA tools, sparse TFA (STFA) is an excellent one, which can obtain a TF spectrum with good readability. However, many STFA algorithms suffer from expensive calculation time and unavoidable prior knowledge, such as the iterative shrinkage-thresholding algorithm (ISTA) and the sparse reconstruction by separable approximation (SpaRSA). Inspired by the unrolled algorithm and its successful applications in signal processing, we propose a deep learning-based ISTA unrolled algorithm, which is named the sparse time-frequency analysis network (STFANet). The STFANet contains two parts, i.e., the sparse time-frequency spectrum generator and the reconstruction module. The former learns how to transform a one-dimensional (1D) seismic signal from a large amount of unlabelled data into a two-dimensional (2D) sparse time-frequency spectrum, which is implemented based on the proposed unrolled iterative dynamic shrinkage-thresholding (UIDST) algorithm. Note that the UIDST algorithm is carried out by using a simplified deep learning network. The latter serves as a physical constraint of model training to ensure that our generator obtains an accurate TF spectrum, which is actually an inverse time-frequency transform. In this study, the traditional inverse short-time Fourier transform (STFT) is utilized in the reconstruction module. To test the effectiveness of the proposed model, we apply it to 3D post-stack field data. The results show that, compared with the traditional TFA tools, the STFANet can availably compute time-frequency spectrum with better readability, which benefits seismic attenuation delineation.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3300578