Signal segmentation using changing regression models with application in seismic engineering
The change detection and segmentation methods have gained considerable attention in scientific research and appear to be the central issue in various application areas. The objective of the paper is to present a segmentation method, based on maximum a posteriori probability (MAP) estimator, with app...
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description | The change detection and segmentation methods have gained considerable attention in scientific research and appear to be the central issue in various application areas. The objective of the paper is to present a segmentation method, based on maximum a posteriori probability (MAP) estimator, with application in seismic signal processing; some interpretations and connections with other approaches in change detection and segmentation, as well as computational aspects in this field are also discussed. The experimental results obtained by Monte Carlo simulations for signal segmentation using different signal models, including models with changes in the mean, in FIR, AR and ARX model parameters, as well as comparisons with other methods, are presented and the effectiveness of the proposed approach is proved. Finally, we discuss an application of segmentation in the analysis of the earthquake records during the Kocaeli seism, Turkey, August 1999, Arcelik station (ARC). The optimal segmentation results are compared with time–frequency analysis, for the reduced interference distribution (RID). The analysis results confirm the efficiency of the segmentation approach used, the change instants resulted by MAP appearing clear in energy and frequency contents of time–frequency distribution.
•We present a method for optimal segmentation with application in seismic signal processing.•Some interpretations and connections with other approaches and computational aspects are discussed.•The case studies and comparisons in simulation confirm the efficiency of the proposed approach.•The segmentation results in seismic signal analysis are compared with the time–frequency analysis.•The approach could provide new physical insight into seismic waves propagation and soil properties. |
doi_str_mv | 10.1016/j.dsp.2013.09.003 |
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•We present a method for optimal segmentation with application in seismic signal processing.•Some interpretations and connections with other approaches and computational aspects are discussed.•The case studies and comparisons in simulation confirm the efficiency of the proposed approach.•The segmentation results in seismic signal analysis are compared with the time–frequency analysis.•The approach could provide new physical insight into seismic waves propagation and soil properties.</description><identifier>ISSN: 1051-2004</identifier><identifier>EISSN: 1095-4333</identifier><identifier>DOI: 10.1016/j.dsp.2013.09.003</identifier><language>eng</language><publisher>Elsevier Inc</publisher><subject>Change detection ; Computer simulation ; Data segmentation ; Digital signal processing ; MAP estimator ; Mathematical models ; Monte Carlo methods ; Monte Carlo simulation ; Regression ; Segmentation ; Seismic phenomena ; Seismic signal processing</subject><ispartof>Digital signal processing, 2014-01, Vol.24, p.14-26</ispartof><rights>2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c330t-3515ba85be3c5aeb67b202917c0550578400d7e5aa7f7ea937465996aab2f5063</citedby><cites>FETCH-LOGICAL-c330t-3515ba85be3c5aeb67b202917c0550578400d7e5aa7f7ea937465996aab2f5063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.dsp.2013.09.003$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Popescu, Theodor D.</creatorcontrib><title>Signal segmentation using changing regression models with application in seismic engineering</title><title>Digital signal processing</title><description>The change detection and segmentation methods have gained considerable attention in scientific research and appear to be the central issue in various application areas. The objective of the paper is to present a segmentation method, based on maximum a posteriori probability (MAP) estimator, with application in seismic signal processing; some interpretations and connections with other approaches in change detection and segmentation, as well as computational aspects in this field are also discussed. The experimental results obtained by Monte Carlo simulations for signal segmentation using different signal models, including models with changes in the mean, in FIR, AR and ARX model parameters, as well as comparisons with other methods, are presented and the effectiveness of the proposed approach is proved. Finally, we discuss an application of segmentation in the analysis of the earthquake records during the Kocaeli seism, Turkey, August 1999, Arcelik station (ARC). The optimal segmentation results are compared with time–frequency analysis, for the reduced interference distribution (RID). The analysis results confirm the efficiency of the segmentation approach used, the change instants resulted by MAP appearing clear in energy and frequency contents of time–frequency distribution.
•We present a method for optimal segmentation with application in seismic signal processing.•Some interpretations and connections with other approaches and computational aspects are discussed.•The case studies and comparisons in simulation confirm the efficiency of the proposed approach.•The segmentation results in seismic signal analysis are compared with the time–frequency analysis.•The approach could provide new physical insight into seismic waves propagation and soil properties.</description><subject>Change detection</subject><subject>Computer simulation</subject><subject>Data segmentation</subject><subject>Digital signal processing</subject><subject>MAP estimator</subject><subject>Mathematical models</subject><subject>Monte Carlo methods</subject><subject>Monte Carlo simulation</subject><subject>Regression</subject><subject>Segmentation</subject><subject>Seismic phenomena</subject><subject>Seismic signal processing</subject><issn>1051-2004</issn><issn>1095-4333</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouH78AG89emmdNE2zxZMsfsGCB_UmhDSddrO0ac10Ff-9LfXsaV6Y9xmYh7ErDgkHnt_sk4qGJAUuEigSAHHEVhwKGWdCiOM5Sx6nANkpOyPaA4DK0nzFPl5d400bETYd-tGMrvfRgZxvIrszvplDwCYg0bzp-gpbir7duIvMMLTOLoTz0wVHnbMRzhBimMgLdlKblvDyb56z94f7t81TvH15fN7cbWMrBIyxkFyWZi1LFFYaLHNVppAWXFmQEqRaZwCVQmmMqhWaQqgsl0WRG1OmtYRcnLPr5e4Q-s8D0qg7Rxbb1njsD6S55CJLxVrCVOVL1YaeKGCth-A6E340Bz2b1Hs9mdSzSQ2FnkxOzO3CTK_jl8OgyTr0FisX0I666t0_9C8kW3zH</recordid><startdate>201401</startdate><enddate>201401</enddate><creator>Popescu, Theodor D.</creator><general>Elsevier Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SM</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201401</creationdate><title>Signal segmentation using changing regression models with application in seismic engineering</title><author>Popescu, Theodor D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c330t-3515ba85be3c5aeb67b202917c0550578400d7e5aa7f7ea937465996aab2f5063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Change detection</topic><topic>Computer simulation</topic><topic>Data segmentation</topic><topic>Digital signal processing</topic><topic>MAP estimator</topic><topic>Mathematical models</topic><topic>Monte Carlo methods</topic><topic>Monte Carlo simulation</topic><topic>Regression</topic><topic>Segmentation</topic><topic>Seismic phenomena</topic><topic>Seismic signal processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Popescu, Theodor D.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Earthquake Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Digital signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Popescu, Theodor D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Signal segmentation using changing regression models with application in seismic engineering</atitle><jtitle>Digital signal processing</jtitle><date>2014-01</date><risdate>2014</risdate><volume>24</volume><spage>14</spage><epage>26</epage><pages>14-26</pages><issn>1051-2004</issn><eissn>1095-4333</eissn><abstract>The change detection and segmentation methods have gained considerable attention in scientific research and appear to be the central issue in various application areas. The objective of the paper is to present a segmentation method, based on maximum a posteriori probability (MAP) estimator, with application in seismic signal processing; some interpretations and connections with other approaches in change detection and segmentation, as well as computational aspects in this field are also discussed. The experimental results obtained by Monte Carlo simulations for signal segmentation using different signal models, including models with changes in the mean, in FIR, AR and ARX model parameters, as well as comparisons with other methods, are presented and the effectiveness of the proposed approach is proved. Finally, we discuss an application of segmentation in the analysis of the earthquake records during the Kocaeli seism, Turkey, August 1999, Arcelik station (ARC). The optimal segmentation results are compared with time–frequency analysis, for the reduced interference distribution (RID). The analysis results confirm the efficiency of the segmentation approach used, the change instants resulted by MAP appearing clear in energy and frequency contents of time–frequency distribution.
•We present a method for optimal segmentation with application in seismic signal processing.•Some interpretations and connections with other approaches and computational aspects are discussed.•The case studies and comparisons in simulation confirm the efficiency of the proposed approach.•The segmentation results in seismic signal analysis are compared with the time–frequency analysis.•The approach could provide new physical insight into seismic waves propagation and soil properties.</abstract><pub>Elsevier Inc</pub><doi>10.1016/j.dsp.2013.09.003</doi><tpages>13</tpages></addata></record> |
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subjects | Change detection Computer simulation Data segmentation Digital signal processing MAP estimator Mathematical models Monte Carlo methods Monte Carlo simulation Regression Segmentation Seismic phenomena Seismic signal processing |
title | Signal segmentation using changing regression models with application in seismic engineering |
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