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Impact of crustal deformation detection by the DSI (difference of split-band interferograms) method with PALSAR-2 data: a case study on the 2016 Kumamoto Earthquake
Interferometric synthetic aperture radar (InSAR) is a useful tool for detecting surface deformations at high spatial resolutions. When InSAR is applied to large surface deformations, clear fringes with complicated phase gaps often appear in the interferograms. Although the surface deformations in su...
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description | Interferometric synthetic aperture radar (InSAR) is a useful tool for detecting surface deformations at high spatial resolutions. When InSAR is applied to large surface deformations, clear fringes with complicated phase gaps often appear in the interferograms. Although the surface deformations in such areas provide valuable data for earthquake research and disaster investigation, it is difficult to convert the complicated interferometric phase to surface deformation information because of the difficulties associated with phase unwrapping. To resolve these difficulties, we created multiple SAR pairs with different frequencies using a bandpass filter and calculated the difference between the interferograms generated from these SAR pairs (referred to as the DSI analysis in this study). Generally, the obtained difference corresponds to SAR observations using long-wavelength radar. Therefore, phase wrap is less likely to occur, simplifying phase unwrapping. We applied the DSI analysis to PALSAR-2 data pairs for the 2016 Kumamoto Earthquake and successfully identified large crustal deformations with complicated phase gaps in the vicinity of the surface ruptures. Comparing these results with the crustal deformations observed with global navigation satellite system measurements, the root-mean-squares of the differences were found to be approximately 4 cm. Although this accuracy was lower than that of conventional InSAR, it was nearly equivalent to that of offset-tracking analysis. It should be noted that the spatial resolution of the DSI analysis was significantly improved compared to that of offset-tracking analysis. A disadvantage of this method is that its detection accuracy is significantly degraded in zones with low coherence owing to noise amplification. The standard deviation of the noise component was approximately 2 cm for pixels with coherence > 0.7. However, for pixels with a coherence 10 cm, and the noise component occasionally exceeded 1 m. Despite its disadvantages, this method is effective for detecting large crustal deformations with high spatial resolution in areas where conventional InSAR processing is inappropriate.
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Graphical Abstract</description><identifier>ISSN: 1880-5981</identifier><identifier>ISSN: 1343-8832</identifier><identifier>EISSN: 1880-5981</identifier><identifier>DOI: 10.1186/s40623-022-01635-0</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>6. Geodesy ; Artificial satellites in remote sensing ; Bandpass filters ; Coherence ; Crustal deformation ; Deformation effects ; Deformations (Mechanics) ; Difference of split-band interferograms ; DSI ; Earth and Environmental Science ; Earth Sciences ; Earthquakes ; Express Letter ; Geology ; Geophysics/Geodesy ; Global navigation satellite system ; InSAR ; Interferometric synthetic aperture radar ; Interferometry ; Japan ; Navigation satellites ; Navigation systems ; Noise ; Noise standards ; Observations ; Phase unwrapping ; Pixels ; Radar ; Satellite observation ; Seismic activity ; Spatial resolution ; Standard deviation ; Synthetic aperture radar ; Synthetic aperture radar interferometry ; The 2016 Kumamoto Earthquake ; Tracking</subject><ispartof>Earth, planets, and space, 2022-05, Vol.74 (1), p.1-11, Article 72</ispartof><rights>The Author(s) 2022</rights><rights>COPYRIGHT 2022 Springer</rights><rights>The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3190-5ab97304119cb5442ef77dade4d7b6792d3cd43404d5a7331ba25e90393b1e473</cites><orcidid>0000-0002-2040-6572</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2666128408/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2666128408?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Ozawa, Taku</creatorcontrib><creatorcontrib>Himematsu, Yuji</creatorcontrib><title>Impact of crustal deformation detection by the DSI (difference of split-band interferograms) method with PALSAR-2 data: a case study on the 2016 Kumamoto Earthquake</title><title>Earth, planets, and space</title><addtitle>Earth Planets Space</addtitle><description>Interferometric synthetic aperture radar (InSAR) is a useful tool for detecting surface deformations at high spatial resolutions. When InSAR is applied to large surface deformations, clear fringes with complicated phase gaps often appear in the interferograms. Although the surface deformations in such areas provide valuable data for earthquake research and disaster investigation, it is difficult to convert the complicated interferometric phase to surface deformation information because of the difficulties associated with phase unwrapping. To resolve these difficulties, we created multiple SAR pairs with different frequencies using a bandpass filter and calculated the difference between the interferograms generated from these SAR pairs (referred to as the DSI analysis in this study). Generally, the obtained difference corresponds to SAR observations using long-wavelength radar. Therefore, phase wrap is less likely to occur, simplifying phase unwrapping. We applied the DSI analysis to PALSAR-2 data pairs for the 2016 Kumamoto Earthquake and successfully identified large crustal deformations with complicated phase gaps in the vicinity of the surface ruptures. Comparing these results with the crustal deformations observed with global navigation satellite system measurements, the root-mean-squares of the differences were found to be approximately 4 cm. Although this accuracy was lower than that of conventional InSAR, it was nearly equivalent to that of offset-tracking analysis. It should be noted that the spatial resolution of the DSI analysis was significantly improved compared to that of offset-tracking analysis. A disadvantage of this method is that its detection accuracy is significantly degraded in zones with low coherence owing to noise amplification. The standard deviation of the noise component was approximately 2 cm for pixels with coherence > 0.7. However, for pixels with a coherence < 0.2, the standard deviation was > 10 cm, and the noise component occasionally exceeded 1 m. Despite its disadvantages, this method is effective for detecting large crustal deformations with high spatial resolution in areas where conventional InSAR processing is inappropriate.
Graphical Abstract</description><subject>6. Geodesy</subject><subject>Artificial satellites in remote sensing</subject><subject>Bandpass filters</subject><subject>Coherence</subject><subject>Crustal deformation</subject><subject>Deformation effects</subject><subject>Deformations (Mechanics)</subject><subject>Difference of split-band interferograms</subject><subject>DSI</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earthquakes</subject><subject>Express Letter</subject><subject>Geology</subject><subject>Geophysics/Geodesy</subject><subject>Global navigation satellite system</subject><subject>InSAR</subject><subject>Interferometric synthetic aperture radar</subject><subject>Interferometry</subject><subject>Japan</subject><subject>Navigation satellites</subject><subject>Navigation systems</subject><subject>Noise</subject><subject>Noise standards</subject><subject>Observations</subject><subject>Phase unwrapping</subject><subject>Pixels</subject><subject>Radar</subject><subject>Satellite observation</subject><subject>Seismic activity</subject><subject>Spatial resolution</subject><subject>Standard deviation</subject><subject>Synthetic aperture radar</subject><subject>Synthetic aperture radar interferometry</subject><subject>The 2016 Kumamoto Earthquake</subject><subject>Tracking</subject><issn>1880-5981</issn><issn>1343-8832</issn><issn>1880-5981</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9UsuOEzEQHCFWYgn8ACdLXOAwi1_z4hYtC0RE2tUunK0eu504ZMZZ2yOU_-FDcTIIOCEf3OquKlVJVRSvGL1irK3fRUlrLkrKeUlZLaqSPikuWdvSsupa9vSf-VnxPMYdpYLKWlwWP1fDAXQi3hIdpphgTwxaHwZIzo95TqjPU38kaYvkw8OKvDHOWgw4ajzx4mHvUtnDaIgbE4Z88psAQ3xLBkxbb8gPl7bkbrl-WN6XnBhI8J4A0RCRxDSZI8n6J3GevZMv0wCDT57cQEjbxwm-44viwsI-4svf_6L49vHm6_Xncn37aXW9XJdasC7Hg75rci7GOt1XUnK0TWPAoDRNXzcdN0IbKSSVpoJGCNYDr7CjohM9Q9mIRbGadY2HnToEN0A4Kg9OnRc-bFT25PQeFQDrdc0b0_a97GoDrZaMWm2bllKbxRfF61nrEPzjhDGpnZ_CmO0rXtc1462kbUZdzagNZFE3Wp8C6PwMDk77Ea3L-2VDZc7FaZUJfCbo4GMMaP_YZFSdqqDmKqhcBXWugqKZJGZSzOBxg-Gvl_-wfgHfG7WN</recordid><startdate>20220518</startdate><enddate>20220518</enddate><creator>Ozawa, Taku</creator><creator>Himematsu, Yuji</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><general>Springer Nature B.V</general><general>SpringerOpen</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2040-6572</orcidid></search><sort><creationdate>20220518</creationdate><title>Impact of crustal deformation detection by the DSI (difference of split-band interferograms) method with PALSAR-2 data: a case study on the 2016 Kumamoto Earthquake</title><author>Ozawa, Taku ; Himematsu, Yuji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3190-5ab97304119cb5442ef77dade4d7b6792d3cd43404d5a7331ba25e90393b1e473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>6. Geodesy</topic><topic>Artificial satellites in remote sensing</topic><topic>Bandpass filters</topic><topic>Coherence</topic><topic>Crustal deformation</topic><topic>Deformation effects</topic><topic>Deformations (Mechanics)</topic><topic>Difference of split-band interferograms</topic><topic>DSI</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Earthquakes</topic><topic>Express Letter</topic><topic>Geology</topic><topic>Geophysics/Geodesy</topic><topic>Global navigation satellite system</topic><topic>InSAR</topic><topic>Interferometric synthetic aperture radar</topic><topic>Interferometry</topic><topic>Japan</topic><topic>Navigation satellites</topic><topic>Navigation systems</topic><topic>Noise</topic><topic>Noise standards</topic><topic>Observations</topic><topic>Phase unwrapping</topic><topic>Pixels</topic><topic>Radar</topic><topic>Satellite observation</topic><topic>Seismic activity</topic><topic>Spatial resolution</topic><topic>Standard deviation</topic><topic>Synthetic aperture radar</topic><topic>Synthetic aperture radar interferometry</topic><topic>The 2016 Kumamoto Earthquake</topic><topic>Tracking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ozawa, Taku</creatorcontrib><creatorcontrib>Himematsu, Yuji</creatorcontrib><collection>SpringerOpen (Open Access)</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni)</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>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Publicly Available Content 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 China</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Earth, planets, and space</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ozawa, Taku</au><au>Himematsu, Yuji</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of crustal deformation detection by the DSI (difference of split-band interferograms) method with PALSAR-2 data: a case study on the 2016 Kumamoto Earthquake</atitle><jtitle>Earth, planets, and space</jtitle><stitle>Earth Planets Space</stitle><date>2022-05-18</date><risdate>2022</risdate><volume>74</volume><issue>1</issue><spage>1</spage><epage>11</epage><pages>1-11</pages><artnum>72</artnum><issn>1880-5981</issn><issn>1343-8832</issn><eissn>1880-5981</eissn><abstract>Interferometric synthetic aperture radar (InSAR) is a useful tool for detecting surface deformations at high spatial resolutions. When InSAR is applied to large surface deformations, clear fringes with complicated phase gaps often appear in the interferograms. Although the surface deformations in such areas provide valuable data for earthquake research and disaster investigation, it is difficult to convert the complicated interferometric phase to surface deformation information because of the difficulties associated with phase unwrapping. To resolve these difficulties, we created multiple SAR pairs with different frequencies using a bandpass filter and calculated the difference between the interferograms generated from these SAR pairs (referred to as the DSI analysis in this study). Generally, the obtained difference corresponds to SAR observations using long-wavelength radar. Therefore, phase wrap is less likely to occur, simplifying phase unwrapping. We applied the DSI analysis to PALSAR-2 data pairs for the 2016 Kumamoto Earthquake and successfully identified large crustal deformations with complicated phase gaps in the vicinity of the surface ruptures. Comparing these results with the crustal deformations observed with global navigation satellite system measurements, the root-mean-squares of the differences were found to be approximately 4 cm. Although this accuracy was lower than that of conventional InSAR, it was nearly equivalent to that of offset-tracking analysis. It should be noted that the spatial resolution of the DSI analysis was significantly improved compared to that of offset-tracking analysis. A disadvantage of this method is that its detection accuracy is significantly degraded in zones with low coherence owing to noise amplification. The standard deviation of the noise component was approximately 2 cm for pixels with coherence > 0.7. However, for pixels with a coherence < 0.2, the standard deviation was > 10 cm, and the noise component occasionally exceeded 1 m. Despite its disadvantages, this method is effective for detecting large crustal deformations with high spatial resolution in areas where conventional InSAR processing is inappropriate.
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subjects | 6. Geodesy Artificial satellites in remote sensing Bandpass filters Coherence Crustal deformation Deformation effects Deformations (Mechanics) Difference of split-band interferograms DSI Earth and Environmental Science Earth Sciences Earthquakes Express Letter Geology Geophysics/Geodesy Global navigation satellite system InSAR Interferometric synthetic aperture radar Interferometry Japan Navigation satellites Navigation systems Noise Noise standards Observations Phase unwrapping Pixels Radar Satellite observation Seismic activity Spatial resolution Standard deviation Synthetic aperture radar Synthetic aperture radar interferometry The 2016 Kumamoto Earthquake Tracking |
title | Impact of crustal deformation detection by the DSI (difference of split-band interferograms) method with PALSAR-2 data: a case study on the 2016 Kumamoto Earthquake |
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