Identification of blasting vibration and coal-rock fracturing microseismic signals
Α new method based on variational mode decomposition (VMD) is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each...
Gespeichert in:
| Veröffentlicht in: | Applied geophysics 2018-06, Vol.15 (2), p.280-289 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 289 |
|---|---|
| container_issue | 2 |
| container_start_page | 280 |
| container_title | Applied geophysics |
| container_volume | 15 |
| creator | Zhang, Xing-Li Jia, Rui-Sheng Lu, Xin-Ming Peng, Yan-Jun Zhao, Wei-Dong |
| description | Α new method based on variational mode decomposition (VMD) is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each mode component is extracted to form the eigenvector of the energy of the original signal and calculate the center of gravity coefficient of the energy distribution plane. Finally, the coal-rock fracturing and blasting vibration signals are classified using a decision tree stump. Experimental results suggest that VMD can effectively separate the signal components into coal-rock fracturing and blasting vibration signals based on frequency. The contrast in the energy distribution center coefficient after the dimension reduction of the energy distribution eigenvector accurately identifies the two types of microseismic signals. The method is verified by comparing it to EMD and wavelet packet decomposition. |
| doi_str_mv | 10.1007/s11770-018-0682-9 |
| format | Article |
| fullrecord | <record><control><sourceid>wanfang_jour_proqu</sourceid><recordid>TN_cdi_wanfang_journals_yydqwl201802013</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><wanfj_id>yydqwl201802013</wanfj_id><sourcerecordid>yydqwl201802013</sourcerecordid><originalsourceid>FETCH-LOGICAL-a371t-5e6eb3d6e0c857ffdb56f957d6c86db55f6ede0a308239c5ff6c7c6fb07c11613</originalsourceid><addsrcrecordid>eNp1kE9LwzAYxoMoOKcfwFvBg6fom8QkzVGGzsFAED2HNE1GZtduSefYtzelwk5e3n_8nofkQeiWwAMBkI-JECkBAykxiJJidYYmRCmWN16e51lIiqWS_BJdpbQGEIyKpwn6WNSu7YMP1vSha4vOF1VjUh_aVfETqjheTVsXtjMNjp39Lnw0tt_HAdkEG7vkQspDkcKqNU26Rhc-N3fz16fo6_Xlc_aGl-_zxex5iQ2TpMfcCVexWjiwJZfe1xUXXnFZC1uKvHAvXO3AMCgpU5Z7L6y0wlcgLSGCsCm6H30PpvWmXel1t4_DA_TxWO8ODc1hQC4sk3cjuY3dbu9Sf0IpKCUV40RmiozU8KUUndfbGDYmHjUBPYSsx5B19tVDyFplDR01aTvk4eLJ-X_RLxTYgFY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2099793517</pqid></control><display><type>article</type><title>Identification of blasting vibration and coal-rock fracturing microseismic signals</title><source>Springer Nature - Complete Springer Journals</source><source>Alma/SFX Local Collection</source><creator>Zhang, Xing-Li ; Jia, Rui-Sheng ; Lu, Xin-Ming ; Peng, Yan-Jun ; Zhao, Wei-Dong</creator><creatorcontrib>Zhang, Xing-Li ; Jia, Rui-Sheng ; Lu, Xin-Ming ; Peng, Yan-Jun ; Zhao, Wei-Dong</creatorcontrib><description>Α new method based on variational mode decomposition (VMD) is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each mode component is extracted to form the eigenvector of the energy of the original signal and calculate the center of gravity coefficient of the energy distribution plane. Finally, the coal-rock fracturing and blasting vibration signals are classified using a decision tree stump. Experimental results suggest that VMD can effectively separate the signal components into coal-rock fracturing and blasting vibration signals based on frequency. The contrast in the energy distribution center coefficient after the dimension reduction of the energy distribution eigenvector accurately identifies the two types of microseismic signals. The method is verified by comparing it to EMD and wavelet packet decomposition.</description><identifier>ISSN: 1672-7975</identifier><identifier>EISSN: 1993-0658</identifier><identifier>DOI: 10.1007/s11770-018-0682-9</identifier><language>eng</language><publisher>Beijing: Chinese Geophysical Society</publisher><subject>Blasting ; Center of gravity ; Coal ; Components ; Decision trees ; Decomposition ; Distribution ; Earth and Environmental Science ; Earth Sciences ; Eigenvectors ; Energy ; Energy distribution ; Fracturing ; Geophysics/Geodesy ; Geotechnical Engineering & Applied Earth Sciences ; Gravity ; Mathematical analysis ; Methods ; Microseisms ; Rocks ; Signal classification ; Vibration ; Wavelet analysis</subject><ispartof>Applied geophysics, 2018-06, Vol.15 (2), p.280-289</ispartof><rights>Editorial Office of Applied Geophysics and Springer-Verlag GmbH Germany, part of Springer Nature 2018</rights><rights>Applied Geophysics is a copyright of Springer, (2018). All Rights Reserved.</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a371t-5e6eb3d6e0c857ffdb56f957d6c86db55f6ede0a308239c5ff6c7c6fb07c11613</citedby><cites>FETCH-LOGICAL-a371t-5e6eb3d6e0c857ffdb56f957d6c86db55f6ede0a308239c5ff6c7c6fb07c11613</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/yydqwl/yydqwl.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11770-018-0682-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11770-018-0682-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Zhang, Xing-Li</creatorcontrib><creatorcontrib>Jia, Rui-Sheng</creatorcontrib><creatorcontrib>Lu, Xin-Ming</creatorcontrib><creatorcontrib>Peng, Yan-Jun</creatorcontrib><creatorcontrib>Zhao, Wei-Dong</creatorcontrib><title>Identification of blasting vibration and coal-rock fracturing microseismic signals</title><title>Applied geophysics</title><addtitle>Appl. Geophys</addtitle><description>Α new method based on variational mode decomposition (VMD) is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each mode component is extracted to form the eigenvector of the energy of the original signal and calculate the center of gravity coefficient of the energy distribution plane. Finally, the coal-rock fracturing and blasting vibration signals are classified using a decision tree stump. Experimental results suggest that VMD can effectively separate the signal components into coal-rock fracturing and blasting vibration signals based on frequency. The contrast in the energy distribution center coefficient after the dimension reduction of the energy distribution eigenvector accurately identifies the two types of microseismic signals. The method is verified by comparing it to EMD and wavelet packet decomposition.</description><subject>Blasting</subject><subject>Center of gravity</subject><subject>Coal</subject><subject>Components</subject><subject>Decision trees</subject><subject>Decomposition</subject><subject>Distribution</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Eigenvectors</subject><subject>Energy</subject><subject>Energy distribution</subject><subject>Fracturing</subject><subject>Geophysics/Geodesy</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Gravity</subject><subject>Mathematical analysis</subject><subject>Methods</subject><subject>Microseisms</subject><subject>Rocks</subject><subject>Signal classification</subject><subject>Vibration</subject><subject>Wavelet analysis</subject><issn>1672-7975</issn><issn>1993-0658</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kE9LwzAYxoMoOKcfwFvBg6fom8QkzVGGzsFAED2HNE1GZtduSefYtzelwk5e3n_8nofkQeiWwAMBkI-JECkBAykxiJJidYYmRCmWN16e51lIiqWS_BJdpbQGEIyKpwn6WNSu7YMP1vSha4vOF1VjUh_aVfETqjheTVsXtjMNjp39Lnw0tt_HAdkEG7vkQspDkcKqNU26Rhc-N3fz16fo6_Xlc_aGl-_zxex5iQ2TpMfcCVexWjiwJZfe1xUXXnFZC1uKvHAvXO3AMCgpU5Z7L6y0wlcgLSGCsCm6H30PpvWmXel1t4_DA_TxWO8ODc1hQC4sk3cjuY3dbu9Sf0IpKCUV40RmiozU8KUUndfbGDYmHjUBPYSsx5B19tVDyFplDR01aTvk4eLJ-X_RLxTYgFY</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Zhang, Xing-Li</creator><creator>Jia, Rui-Sheng</creator><creator>Lu, Xin-Ming</creator><creator>Peng, Yan-Jun</creator><creator>Zhao, Wei-Dong</creator><general>Chinese Geophysical Society</general><general>Springer Nature B.V</general><general>College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China</general><general>Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao 266590, China</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>L7M</scope><scope>M2P</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20180601</creationdate><title>Identification of blasting vibration and coal-rock fracturing microseismic signals</title><author>Zhang, Xing-Li ; Jia, Rui-Sheng ; Lu, Xin-Ming ; Peng, Yan-Jun ; Zhao, Wei-Dong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a371t-5e6eb3d6e0c857ffdb56f957d6c86db55f6ede0a308239c5ff6c7c6fb07c11613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Blasting</topic><topic>Center of gravity</topic><topic>Coal</topic><topic>Components</topic><topic>Decision trees</topic><topic>Decomposition</topic><topic>Distribution</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Eigenvectors</topic><topic>Energy</topic><topic>Energy distribution</topic><topic>Fracturing</topic><topic>Geophysics/Geodesy</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Gravity</topic><topic>Mathematical analysis</topic><topic>Methods</topic><topic>Microseisms</topic><topic>Rocks</topic><topic>Signal classification</topic><topic>Vibration</topic><topic>Wavelet analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Xing-Li</creatorcontrib><creatorcontrib>Jia, Rui-Sheng</creatorcontrib><creatorcontrib>Lu, Xin-Ming</creatorcontrib><creatorcontrib>Peng, Yan-Jun</creatorcontrib><creatorcontrib>Zhao, Wei-Dong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Applied geophysics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Xing-Li</au><au>Jia, Rui-Sheng</au><au>Lu, Xin-Ming</au><au>Peng, Yan-Jun</au><au>Zhao, Wei-Dong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of blasting vibration and coal-rock fracturing microseismic signals</atitle><jtitle>Applied geophysics</jtitle><stitle>Appl. Geophys</stitle><date>2018-06-01</date><risdate>2018</risdate><volume>15</volume><issue>2</issue><spage>280</spage><epage>289</epage><pages>280-289</pages><issn>1672-7975</issn><eissn>1993-0658</eissn><abstract>Α new method based on variational mode decomposition (VMD) is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each mode component is extracted to form the eigenvector of the energy of the original signal and calculate the center of gravity coefficient of the energy distribution plane. Finally, the coal-rock fracturing and blasting vibration signals are classified using a decision tree stump. Experimental results suggest that VMD can effectively separate the signal components into coal-rock fracturing and blasting vibration signals based on frequency. The contrast in the energy distribution center coefficient after the dimension reduction of the energy distribution eigenvector accurately identifies the two types of microseismic signals. The method is verified by comparing it to EMD and wavelet packet decomposition.</abstract><cop>Beijing</cop><pub>Chinese Geophysical Society</pub><doi>10.1007/s11770-018-0682-9</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1672-7975 |
| ispartof | Applied geophysics, 2018-06, Vol.15 (2), p.280-289 |
| issn | 1672-7975 1993-0658 |
| language | eng |
| recordid | cdi_wanfang_journals_yydqwl201802013 |
| source | Springer Nature - Complete Springer Journals; Alma/SFX Local Collection |
| subjects | Blasting Center of gravity Coal Components Decision trees Decomposition Distribution Earth and Environmental Science Earth Sciences Eigenvectors Energy Energy distribution Fracturing Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Gravity Mathematical analysis Methods Microseisms Rocks Signal classification Vibration Wavelet analysis |
| title | Identification of blasting vibration and coal-rock fracturing microseismic signals |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T07%3A13%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-wanfang_jour_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Identification%20of%20blasting%20vibration%20and%20coal-rock%20fracturing%20microseismic%20signals&rft.jtitle=Applied%20geophysics&rft.au=Zhang,%20Xing-Li&rft.date=2018-06-01&rft.volume=15&rft.issue=2&rft.spage=280&rft.epage=289&rft.pages=280-289&rft.issn=1672-7975&rft.eissn=1993-0658&rft_id=info:doi/10.1007/s11770-018-0682-9&rft_dat=%3Cwanfang_jour_proqu%3Eyydqwl201802013%3C/wanfang_jour_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2099793517&rft_id=info:pmid/&rft_wanfj_id=yydqwl201802013&rfr_iscdi=true |