3-D Joint Inversion of Airborne Electromagnetic and Magnetic Data Based on Local Pearson Correlation Constraints
Based on the spatial structure correlation in different geophysical parameters, we propose a new 3-D joint inversion method for frequency-domain airborne electromagnetic (AEM) and airborne magnetic (AirMag) data by incorporating a local Pearson correlation constraint (LPCC). For each iteration, the...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-13 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 13 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | IEEE transactions on geoscience and remote sensing |
| container_volume | 60 |
| creator | Liu, Yunhe Na, Xu Yin, Changchun Su, Yang Sun, Siyuan Zhang, Bo Ren, Xiuyan Baranwal, Vikas Chand |
| description | Based on the spatial structure correlation in different geophysical parameters, we propose a new 3-D joint inversion method for frequency-domain airborne electromagnetic (AEM) and airborne magnetic (AirMag) data by incorporating a local Pearson correlation constraint (LPCC). For each iteration, the entire model is separated into multiple subdomains and the Pearson correlation coefficients of resistivity and magnetization in the subdomain are employed as the additional regularization term to do the joint constraint. This new regularization term is continuously updated in the inversion process to ensure that the resistivity and magnetization models in two separated inversions converge to a similar spatial structure. As a statistics technology, the LPCC-based joint inversion scheme not only has the advantages of the conventional joint inversions, but also can implement the structural constraints in different scales by selecting different sizes of the subdomain. This provides the flexibility for solving multiscale problems. Synthetic examples show that the joint inversion can improve the overall inversion resolution by combining the high vertical resolution of the EM method and large exploration depth and high horizontal resolution of the magnetic method. In the application to field survey datasets, the joint inversion delivers better results than those of separate inversions, which further verifies the effectiveness of our method. |
| doi_str_mv | 10.1109/TGRS.2022.3143659 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9682728</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9682728</ieee_id><sourcerecordid>2639933825</sourcerecordid><originalsourceid>FETCH-LOGICAL-c223t-d6a01c29326c228a8fb34f65a2f5bbc9567b19f628428210a5a9cd6bca2f25aa3</originalsourceid><addsrcrecordid>eNo9kEFPAjEQhRujiYj-AOOliefFdrot7REREYPRKJ43s92uWQJbbIuJ_95F0NPMm7w3L_kIueRswDkzN4vp69sAGMBA8FwoaY5Ij0upM6by_Jj0GDcqA23glJzFuGSM55IPe2Qjsjv66Js20Vn75UJsfEt9TUdNKH1oHZ2snE3Br_GjdamxFNuKPv2JO0xIbzG6inaxube4oi8OQ-zU2IfgVpia372NKWDXEs_JSY2r6C4Os0_e7yeL8UM2f57OxqN5ZgFEyiqFjFswAlR30KjrUuS1kgi1LEtrpBqW3NQKdA4aOEOJxlaqtJ0BJKLok-v9303wn1sXU7H029B2lQUoYYwQGmTn4nuXDT7G4OpiE5o1hu-Cs2IHttiBLXZgiwPYLnO1zzTOuX-_URqGoMUPfnF0rA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2639933825</pqid></control><display><type>article</type><title>3-D Joint Inversion of Airborne Electromagnetic and Magnetic Data Based on Local Pearson Correlation Constraints</title><source>IEEE Electronic Library (IEL)</source><creator>Liu, Yunhe ; Na, Xu ; Yin, Changchun ; Su, Yang ; Sun, Siyuan ; Zhang, Bo ; Ren, Xiuyan ; Baranwal, Vikas Chand</creator><creatorcontrib>Liu, Yunhe ; Na, Xu ; Yin, Changchun ; Su, Yang ; Sun, Siyuan ; Zhang, Bo ; Ren, Xiuyan ; Baranwal, Vikas Chand</creatorcontrib><description>Based on the spatial structure correlation in different geophysical parameters, we propose a new 3-D joint inversion method for frequency-domain airborne electromagnetic (AEM) and airborne magnetic (AirMag) data by incorporating a local Pearson correlation constraint (LPCC). For each iteration, the entire model is separated into multiple subdomains and the Pearson correlation coefficients of resistivity and magnetization in the subdomain are employed as the additional regularization term to do the joint constraint. This new regularization term is continuously updated in the inversion process to ensure that the resistivity and magnetization models in two separated inversions converge to a similar spatial structure. As a statistics technology, the LPCC-based joint inversion scheme not only has the advantages of the conventional joint inversions, but also can implement the structural constraints in different scales by selecting different sizes of the subdomain. This provides the flexibility for solving multiscale problems. Synthetic examples show that the joint inversion can improve the overall inversion resolution by combining the high vertical resolution of the EM method and large exploration depth and high horizontal resolution of the magnetic method. In the application to field survey datasets, the joint inversion delivers better results than those of separate inversions, which further verifies the effectiveness of our method.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2022.3143659</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>3-D joint inversion ; airborne electromagnetic (AEM) ; airborne magnetic (AirMag) ; Atmospheric modeling ; Coefficients ; Correlation ; Correlation coefficient ; Correlation coefficients ; Electrical resistivity ; Inversions ; Iterative methods ; Linear programming ; local Pearson correlation coefficient (PCC) ; Magnetic data ; Magnetic methods ; Magnetic separation ; Magnetization ; Mathematical models ; Regularization ; Resolution ; Statistical methods ; Surveying ; Three-dimensional displays</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-13</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c223t-d6a01c29326c228a8fb34f65a2f5bbc9567b19f628428210a5a9cd6bca2f25aa3</citedby><cites>FETCH-LOGICAL-c223t-d6a01c29326c228a8fb34f65a2f5bbc9567b19f628428210a5a9cd6bca2f25aa3</cites><orcidid>0000-0001-5709-7294 ; 0000-0002-3760-6562 ; 0000-0003-4046-1015 ; 0000-0001-8685-5584 ; 0000-0002-0704-9806 ; 0000-0002-3634-9832</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9682728$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,4010,27900,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9682728$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Liu, Yunhe</creatorcontrib><creatorcontrib>Na, Xu</creatorcontrib><creatorcontrib>Yin, Changchun</creatorcontrib><creatorcontrib>Su, Yang</creatorcontrib><creatorcontrib>Sun, Siyuan</creatorcontrib><creatorcontrib>Zhang, Bo</creatorcontrib><creatorcontrib>Ren, Xiuyan</creatorcontrib><creatorcontrib>Baranwal, Vikas Chand</creatorcontrib><title>3-D Joint Inversion of Airborne Electromagnetic and Magnetic Data Based on Local Pearson Correlation Constraints</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>Based on the spatial structure correlation in different geophysical parameters, we propose a new 3-D joint inversion method for frequency-domain airborne electromagnetic (AEM) and airborne magnetic (AirMag) data by incorporating a local Pearson correlation constraint (LPCC). For each iteration, the entire model is separated into multiple subdomains and the Pearson correlation coefficients of resistivity and magnetization in the subdomain are employed as the additional regularization term to do the joint constraint. This new regularization term is continuously updated in the inversion process to ensure that the resistivity and magnetization models in two separated inversions converge to a similar spatial structure. As a statistics technology, the LPCC-based joint inversion scheme not only has the advantages of the conventional joint inversions, but also can implement the structural constraints in different scales by selecting different sizes of the subdomain. This provides the flexibility for solving multiscale problems. Synthetic examples show that the joint inversion can improve the overall inversion resolution by combining the high vertical resolution of the EM method and large exploration depth and high horizontal resolution of the magnetic method. In the application to field survey datasets, the joint inversion delivers better results than those of separate inversions, which further verifies the effectiveness of our method.</description><subject>3-D joint inversion</subject><subject>airborne electromagnetic (AEM)</subject><subject>airborne magnetic (AirMag)</subject><subject>Atmospheric modeling</subject><subject>Coefficients</subject><subject>Correlation</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Electrical resistivity</subject><subject>Inversions</subject><subject>Iterative methods</subject><subject>Linear programming</subject><subject>local Pearson correlation coefficient (PCC)</subject><subject>Magnetic data</subject><subject>Magnetic methods</subject><subject>Magnetic separation</subject><subject>Magnetization</subject><subject>Mathematical models</subject><subject>Regularization</subject><subject>Resolution</subject><subject>Statistical methods</subject><subject>Surveying</subject><subject>Three-dimensional displays</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEFPAjEQhRujiYj-AOOliefFdrot7REREYPRKJ43s92uWQJbbIuJ_95F0NPMm7w3L_kIueRswDkzN4vp69sAGMBA8FwoaY5Ij0upM6by_Jj0GDcqA23glJzFuGSM55IPe2Qjsjv66Js20Vn75UJsfEt9TUdNKH1oHZ2snE3Br_GjdamxFNuKPv2JO0xIbzG6inaxube4oi8OQ-zU2IfgVpia372NKWDXEs_JSY2r6C4Os0_e7yeL8UM2f57OxqN5ZgFEyiqFjFswAlR30KjrUuS1kgi1LEtrpBqW3NQKdA4aOEOJxlaqtJ0BJKLok-v9303wn1sXU7H029B2lQUoYYwQGmTn4nuXDT7G4OpiE5o1hu-Cs2IHttiBLXZgiwPYLnO1zzTOuX-_URqGoMUPfnF0rA</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Liu, Yunhe</creator><creator>Na, Xu</creator><creator>Yin, Changchun</creator><creator>Su, Yang</creator><creator>Sun, Siyuan</creator><creator>Zhang, Bo</creator><creator>Ren, Xiuyan</creator><creator>Baranwal, Vikas Chand</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-5709-7294</orcidid><orcidid>https://orcid.org/0000-0002-3760-6562</orcidid><orcidid>https://orcid.org/0000-0003-4046-1015</orcidid><orcidid>https://orcid.org/0000-0001-8685-5584</orcidid><orcidid>https://orcid.org/0000-0002-0704-9806</orcidid><orcidid>https://orcid.org/0000-0002-3634-9832</orcidid></search><sort><creationdate>2022</creationdate><title>3-D Joint Inversion of Airborne Electromagnetic and Magnetic Data Based on Local Pearson Correlation Constraints</title><author>Liu, Yunhe ; Na, Xu ; Yin, Changchun ; Su, Yang ; Sun, Siyuan ; Zhang, Bo ; Ren, Xiuyan ; Baranwal, Vikas Chand</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c223t-d6a01c29326c228a8fb34f65a2f5bbc9567b19f628428210a5a9cd6bca2f25aa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>3-D joint inversion</topic><topic>airborne electromagnetic (AEM)</topic><topic>airborne magnetic (AirMag)</topic><topic>Atmospheric modeling</topic><topic>Coefficients</topic><topic>Correlation</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Electrical resistivity</topic><topic>Inversions</topic><topic>Iterative methods</topic><topic>Linear programming</topic><topic>local Pearson correlation coefficient (PCC)</topic><topic>Magnetic data</topic><topic>Magnetic methods</topic><topic>Magnetic separation</topic><topic>Magnetization</topic><topic>Mathematical models</topic><topic>Regularization</topic><topic>Resolution</topic><topic>Statistical methods</topic><topic>Surveying</topic><topic>Three-dimensional displays</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yunhe</creatorcontrib><creatorcontrib>Na, Xu</creatorcontrib><creatorcontrib>Yin, Changchun</creatorcontrib><creatorcontrib>Su, Yang</creatorcontrib><creatorcontrib>Sun, Siyuan</creatorcontrib><creatorcontrib>Zhang, Bo</creatorcontrib><creatorcontrib>Ren, Xiuyan</creatorcontrib><creatorcontrib>Baranwal, Vikas Chand</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liu, Yunhe</au><au>Na, Xu</au><au>Yin, Changchun</au><au>Su, Yang</au><au>Sun, Siyuan</au><au>Zhang, Bo</au><au>Ren, Xiuyan</au><au>Baranwal, Vikas Chand</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>3-D Joint Inversion of Airborne Electromagnetic and Magnetic Data Based on Local Pearson Correlation Constraints</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2022</date><risdate>2022</risdate><volume>60</volume><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract>Based on the spatial structure correlation in different geophysical parameters, we propose a new 3-D joint inversion method for frequency-domain airborne electromagnetic (AEM) and airborne magnetic (AirMag) data by incorporating a local Pearson correlation constraint (LPCC). For each iteration, the entire model is separated into multiple subdomains and the Pearson correlation coefficients of resistivity and magnetization in the subdomain are employed as the additional regularization term to do the joint constraint. This new regularization term is continuously updated in the inversion process to ensure that the resistivity and magnetization models in two separated inversions converge to a similar spatial structure. As a statistics technology, the LPCC-based joint inversion scheme not only has the advantages of the conventional joint inversions, but also can implement the structural constraints in different scales by selecting different sizes of the subdomain. This provides the flexibility for solving multiscale problems. Synthetic examples show that the joint inversion can improve the overall inversion resolution by combining the high vertical resolution of the EM method and large exploration depth and high horizontal resolution of the magnetic method. In the application to field survey datasets, the joint inversion delivers better results than those of separate inversions, which further verifies the effectiveness of our method.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2022.3143659</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-5709-7294</orcidid><orcidid>https://orcid.org/0000-0002-3760-6562</orcidid><orcidid>https://orcid.org/0000-0003-4046-1015</orcidid><orcidid>https://orcid.org/0000-0001-8685-5584</orcidid><orcidid>https://orcid.org/0000-0002-0704-9806</orcidid><orcidid>https://orcid.org/0000-0002-3634-9832</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0196-2892 |
| ispartof | IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-13 |
| issn | 0196-2892 1558-0644 |
| language | eng |
| recordid | cdi_ieee_primary_9682728 |
| source | IEEE Electronic Library (IEL) |
| subjects | 3-D joint inversion airborne electromagnetic (AEM) airborne magnetic (AirMag) Atmospheric modeling Coefficients Correlation Correlation coefficient Correlation coefficients Electrical resistivity Inversions Iterative methods Linear programming local Pearson correlation coefficient (PCC) Magnetic data Magnetic methods Magnetic separation Magnetization Mathematical models Regularization Resolution Statistical methods Surveying Three-dimensional displays |
| title | 3-D Joint Inversion of Airborne Electromagnetic and Magnetic Data Based on Local Pearson Correlation Constraints |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T00%3A04%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=3-D%20Joint%20Inversion%20of%20Airborne%20Electromagnetic%20and%20Magnetic%20Data%20Based%20on%20Local%20Pearson%20Correlation%20Constraints&rft.jtitle=IEEE%20transactions%20on%20geoscience%20and%20remote%20sensing&rft.au=Liu,%20Yunhe&rft.date=2022&rft.volume=60&rft.spage=1&rft.epage=13&rft.pages=1-13&rft.issn=0196-2892&rft.eissn=1558-0644&rft.coden=IGRSD2&rft_id=info:doi/10.1109/TGRS.2022.3143659&rft_dat=%3Cproquest_RIE%3E2639933825%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2639933825&rft_id=info:pmid/&rft_ieee_id=9682728&rfr_iscdi=true |