{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:18:39Z","timestamp":1760523519724,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,6,7]],"date-time":"2017-06-07T00:00:00Z","timestamp":1496793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Nature Science Foundation of China","award":["61102064"],"award-info":[{"award-number":["61102064"]}]},{"name":"the Basic Research (Free Exploration) Project from Commission on Innovation and Technology of Shenzhen"},{"name":"Key Laboratory of Satellite Mapping Technology and Application, National Administration of Surveying, Mapping and Geoinformation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/a10020065","type":"journal-article","created":{"date-parts":[[2017,6,7]],"date-time":"2017-06-07T10:01:20Z","timestamp":1496829680000},"page":"65","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering"],"prefix":"10.3390","volume":"10","author":[{"given":"Xin","family":"Tian","sequence":"first","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Song","family":"Li","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.measurement.2015.03.041","article-title":"Use of composite higher order spectra for faults diagnosis of rotating machines with different foundation flexibilities","volume":"70","author":"Sinha","year":"2015","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1177\/1475921716651394","article-title":"Sensitivity analysis of higher order coherent spectra in machine faults diagnosis","volume":"15","author":"Sinha","year":"2016","journal-title":"Struct. 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