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Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

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Electronic Information School, Wuhan University, Wuhan 430072, China
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Academic Editor: Girija Chetty
Algorithms 2017, 10(2), 65; https://doi.org/10.3390/a10020065
Received: 28 March 2017 / Revised: 25 May 2017 / Accepted: 31 May 2017 / Published: 7 June 2017
We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments. View Full-Text
Keywords: seismic signal compression; nonparametric bayesian dictionary learning; clustering; sparse representation seismic signal compression; nonparametric bayesian dictionary learning; clustering; sparse representation
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Tian, X.; Li, S. Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering. Algorithms 2017, 10, 65.

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