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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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 61; S. 1
Hauptverfasser:	Liu, Naihao, Lei, Youbo, Liu, Rongchang, Yang, Yang, Wei, Tao, Gao, Jinghuai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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