Loading…
Generative Modeling of InSAR Interferograms
Interferometric synthetic aperture radar (InSAR) has become an essential technique to detect surface variations due to volcanoes, earthquakes, landslides, glaciers, and aquifers. However, Earth's ionosphere, atmosphere, vegetation, surface runoff, etc., introduce noise that requires post‐proces...
Saved in:
Published in: | Earth and space science (Hoboken, N.J.) N.J.), 2019-12, Vol.6 (12), p.2671-2683 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-a5432-f587c7d16e4bc798102d0719ef54f3c3ae7d4f44c1a3b039218075f216b62a753 |
---|---|
cites | cdi_FETCH-LOGICAL-a5432-f587c7d16e4bc798102d0719ef54f3c3ae7d4f44c1a3b039218075f216b62a753 |
container_end_page | 2683 |
container_issue | 12 |
container_start_page | 2671 |
container_title | Earth and space science (Hoboken, N.J.) |
container_volume | 6 |
creator | Rongier, Guillaume Rude, Cody Herring, Thomas Pankratius, Victor |
description | Interferometric synthetic aperture radar (InSAR) has become an essential technique to detect surface variations due to volcanoes, earthquakes, landslides, glaciers, and aquifers. However, Earth's ionosphere, atmosphere, vegetation, surface runoff, etc., introduce noise that requires post‐processing to separate its components. This work defines a generator to create interferograms that include each of those components. Our approach leverages deformation models with real data, either directly or through machine learning using geostatistical methods. These methods result from previous developments to more efficiently and better simulate spatial variables and could replace some statistical approaches used in InSAR processing. We illustrate the use of the generator to simulate an artificial interferogram based on the 2015 Illapel earthquake and discuss the improved performance offered by geostatistical approaches compared with classical statistical ones. The generator establishes a tool for multiple applications (1) to evaluate InSAR correction workflows in controlled scenarios with known ground truth; (2) to develop training sets and generative methods for machine learning algorithms; and (3) to educate on InSAR and its principles.
Key Points
We introduce a software tool that can generate artificial interferograms for synthetic aperture radar (SAR) applications
The tool leverages real data and geostatistical methods to generate and perturb interferogram components
It can be used to evaluate InSAR error correction workflows, to enhance machine learning use with InSAR, and to teach InSAR principles |
doi_str_mv | 10.1029/2018EA000533 |
format | article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_e4c58809c7c1413184554cff31d42cd9</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_e4c58809c7c1413184554cff31d42cd9</doaj_id><sourcerecordid>2344184918</sourcerecordid><originalsourceid>FETCH-LOGICAL-a5432-f587c7d16e4bc798102d0719ef54f3c3ae7d4f44c1a3b039218075f216b62a753</originalsourceid><addsrcrecordid>eNp9kc9LHDEYhoO0VLHeepaFXgTd-n35MclchEVWu2ApuO05ZDNftrPMTjSZtfjfN3atrB56SULy8PB-eRn7hPAFgdfnHNBMJwCghNhjB1wIMVZg5Lud8z47ynlVGOSqAi4_sH3BoZIC1AE7vaaekhvaBxp9iw11bb8cxTCa9fPJbVkHSoFSXCa3zh_Z--C6TEfP-yH7eTX9cfl1fPP9enY5uRk7JQUfB2W01w1WJBde16YEbUBjTUHJILxwpBsZpPToxAJEzdGAVoFjtai400ocstnW20S3snepXbv0aKNr7d-LmJbWpaH1HVmSXhkDtdceJQo0UinpQxDYSO6burgutq67zWJNjad-SK57JX390re_7DI-WA1QaQVFcPIsSPF-Q3mw6zZ76jrXU9xky4WqKiwFPKGf36CruEl9-apCSVnC1WgKdbalfIo5JwovYRDsU6l2t9SCH-8O8AL_q7AAuAV-tx09_ldmp_M5l5yLP9tlpp8</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2344184918</pqid></control><display><type>article</type><title>Generative Modeling of InSAR Interferograms</title><source>Wiley_OA刊</source><source>Publicly Available Content (ProQuest)</source><creator>Rongier, Guillaume ; Rude, Cody ; Herring, Thomas ; Pankratius, Victor</creator><creatorcontrib>Rongier, Guillaume ; Rude, Cody ; Herring, Thomas ; Pankratius, Victor</creatorcontrib><description>Interferometric synthetic aperture radar (InSAR) has become an essential technique to detect surface variations due to volcanoes, earthquakes, landslides, glaciers, and aquifers. However, Earth's ionosphere, atmosphere, vegetation, surface runoff, etc., introduce noise that requires post‐processing to separate its components. This work defines a generator to create interferograms that include each of those components. Our approach leverages deformation models with real data, either directly or through machine learning using geostatistical methods. These methods result from previous developments to more efficiently and better simulate spatial variables and could replace some statistical approaches used in InSAR processing. We illustrate the use of the generator to simulate an artificial interferogram based on the 2015 Illapel earthquake and discuss the improved performance offered by geostatistical approaches compared with classical statistical ones. The generator establishes a tool for multiple applications (1) to evaluate InSAR correction workflows in controlled scenarios with known ground truth; (2) to develop training sets and generative methods for machine learning algorithms; and (3) to educate on InSAR and its principles.
Key Points
We introduce a software tool that can generate artificial interferograms for synthetic aperture radar (SAR) applications
The tool leverages real data and geostatistical methods to generate and perturb interferogram components
It can be used to evaluate InSAR error correction workflows, to enhance machine learning use with InSAR, and to teach InSAR principles</description><identifier>ISSN: 2333-5084</identifier><identifier>EISSN: 2333-5084</identifier><identifier>DOI: 10.1029/2018EA000533</identifier><identifier>PMID: 32064305</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Avalanches ; Cryosphere ; Deformation ; Earthquake Ground Motions and Engineering Seismology ; Earthquakes ; Effusive Volcanism ; Error correction & detection ; Explosive Volcanism ; generator ; Geodesy and Gravity ; Geological ; geostatistics ; Glaciers ; Glaciohydrology ; Informatics ; InSAR ; Instruments and Techniques ; Ionosphere ; Landslides ; Machine learning ; Mathematical Geophysics ; Mud Volcanism ; Natural Hazards ; Noise ; Oceanography: Physical ; Seismic activity ; Seismology ; surface deformation ; Surface runoff ; Technical Reports: Methods ; Topography ; Tsunamis and Storm Surges ; Values ; Volcanic Hazards and Risks ; Volcano Monitoring ; Volcano Seismology ; Volcanoes ; Volcanology ; Workflow</subject><ispartof>Earth and space science (Hoboken, N.J.), 2019-12, Vol.6 (12), p.2671-2683</ispartof><rights>2019. The Authors.</rights><rights>2019. 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><citedby>FETCH-LOGICAL-a5432-f587c7d16e4bc798102d0719ef54f3c3ae7d4f44c1a3b039218075f216b62a753</citedby><cites>FETCH-LOGICAL-a5432-f587c7d16e4bc798102d0719ef54f3c3ae7d4f44c1a3b039218075f216b62a753</cites><orcidid>0000-0002-4658-6583 ; 0000-0002-5910-6868 ; 0000-0002-6030-0545 ; 0000-0002-9584-2600</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2344184918/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2344184918?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,11562,25753,27924,27925,37012,37013,44590,46052,46476,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32064305$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rongier, Guillaume</creatorcontrib><creatorcontrib>Rude, Cody</creatorcontrib><creatorcontrib>Herring, Thomas</creatorcontrib><creatorcontrib>Pankratius, Victor</creatorcontrib><title>Generative Modeling of InSAR Interferograms</title><title>Earth and space science (Hoboken, N.J.)</title><addtitle>Earth Space Sci</addtitle><description>Interferometric synthetic aperture radar (InSAR) has become an essential technique to detect surface variations due to volcanoes, earthquakes, landslides, glaciers, and aquifers. However, Earth's ionosphere, atmosphere, vegetation, surface runoff, etc., introduce noise that requires post‐processing to separate its components. This work defines a generator to create interferograms that include each of those components. Our approach leverages deformation models with real data, either directly or through machine learning using geostatistical methods. These methods result from previous developments to more efficiently and better simulate spatial variables and could replace some statistical approaches used in InSAR processing. We illustrate the use of the generator to simulate an artificial interferogram based on the 2015 Illapel earthquake and discuss the improved performance offered by geostatistical approaches compared with classical statistical ones. The generator establishes a tool for multiple applications (1) to evaluate InSAR correction workflows in controlled scenarios with known ground truth; (2) to develop training sets and generative methods for machine learning algorithms; and (3) to educate on InSAR and its principles.
Key Points
We introduce a software tool that can generate artificial interferograms for synthetic aperture radar (SAR) applications
The tool leverages real data and geostatistical methods to generate and perturb interferogram components
It can be used to evaluate InSAR error correction workflows, to enhance machine learning use with InSAR, and to teach InSAR principles</description><subject>Algorithms</subject><subject>Avalanches</subject><subject>Cryosphere</subject><subject>Deformation</subject><subject>Earthquake Ground Motions and Engineering Seismology</subject><subject>Earthquakes</subject><subject>Effusive Volcanism</subject><subject>Error correction & detection</subject><subject>Explosive Volcanism</subject><subject>generator</subject><subject>Geodesy and Gravity</subject><subject>Geological</subject><subject>geostatistics</subject><subject>Glaciers</subject><subject>Glaciohydrology</subject><subject>Informatics</subject><subject>InSAR</subject><subject>Instruments and Techniques</subject><subject>Ionosphere</subject><subject>Landslides</subject><subject>Machine learning</subject><subject>Mathematical Geophysics</subject><subject>Mud Volcanism</subject><subject>Natural Hazards</subject><subject>Noise</subject><subject>Oceanography: Physical</subject><subject>Seismic activity</subject><subject>Seismology</subject><subject>surface deformation</subject><subject>Surface runoff</subject><subject>Technical Reports: Methods</subject><subject>Topography</subject><subject>Tsunamis and Storm Surges</subject><subject>Values</subject><subject>Volcanic Hazards and Risks</subject><subject>Volcano Monitoring</subject><subject>Volcano Seismology</subject><subject>Volcanoes</subject><subject>Volcanology</subject><subject>Workflow</subject><issn>2333-5084</issn><issn>2333-5084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9kc9LHDEYhoO0VLHeepaFXgTd-n35MclchEVWu2ApuO05ZDNftrPMTjSZtfjfN3atrB56SULy8PB-eRn7hPAFgdfnHNBMJwCghNhjB1wIMVZg5Lud8z47ynlVGOSqAi4_sH3BoZIC1AE7vaaekhvaBxp9iw11bb8cxTCa9fPJbVkHSoFSXCa3zh_Z--C6TEfP-yH7eTX9cfl1fPP9enY5uRk7JQUfB2W01w1WJBde16YEbUBjTUHJILxwpBsZpPToxAJEzdGAVoFjtai400ocstnW20S3snepXbv0aKNr7d-LmJbWpaH1HVmSXhkDtdceJQo0UinpQxDYSO6burgutq67zWJNjad-SK57JX390re_7DI-WA1QaQVFcPIsSPF-Q3mw6zZ76jrXU9xky4WqKiwFPKGf36CruEl9-apCSVnC1WgKdbalfIo5JwovYRDsU6l2t9SCH-8O8AL_q7AAuAV-tx09_ldmp_M5l5yLP9tlpp8</recordid><startdate>201912</startdate><enddate>201912</enddate><creator>Rongier, Guillaume</creator><creator>Rude, Cody</creator><creator>Herring, Thomas</creator><creator>Pankratius, Victor</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><general>American Geophysical Union (AGU)</general><scope>24P</scope><scope>WIN</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4658-6583</orcidid><orcidid>https://orcid.org/0000-0002-5910-6868</orcidid><orcidid>https://orcid.org/0000-0002-6030-0545</orcidid><orcidid>https://orcid.org/0000-0002-9584-2600</orcidid></search><sort><creationdate>201912</creationdate><title>Generative Modeling of InSAR Interferograms</title><author>Rongier, Guillaume ; Rude, Cody ; Herring, Thomas ; Pankratius, Victor</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a5432-f587c7d16e4bc798102d0719ef54f3c3ae7d4f44c1a3b039218075f216b62a753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Avalanches</topic><topic>Cryosphere</topic><topic>Deformation</topic><topic>Earthquake Ground Motions and Engineering Seismology</topic><topic>Earthquakes</topic><topic>Effusive Volcanism</topic><topic>Error correction & detection</topic><topic>Explosive Volcanism</topic><topic>generator</topic><topic>Geodesy and Gravity</topic><topic>Geological</topic><topic>geostatistics</topic><topic>Glaciers</topic><topic>Glaciohydrology</topic><topic>Informatics</topic><topic>InSAR</topic><topic>Instruments and Techniques</topic><topic>Ionosphere</topic><topic>Landslides</topic><topic>Machine learning</topic><topic>Mathematical Geophysics</topic><topic>Mud Volcanism</topic><topic>Natural Hazards</topic><topic>Noise</topic><topic>Oceanography: Physical</topic><topic>Seismic activity</topic><topic>Seismology</topic><topic>surface deformation</topic><topic>Surface runoff</topic><topic>Technical Reports: Methods</topic><topic>Topography</topic><topic>Tsunamis and Storm Surges</topic><topic>Values</topic><topic>Volcanic Hazards and Risks</topic><topic>Volcano Monitoring</topic><topic>Volcano Seismology</topic><topic>Volcanoes</topic><topic>Volcanology</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rongier, Guillaume</creatorcontrib><creatorcontrib>Rude, Cody</creatorcontrib><creatorcontrib>Herring, Thomas</creatorcontrib><creatorcontrib>Pankratius, Victor</creatorcontrib><collection>Wiley_OA刊</collection><collection>Wiley Online Library website</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</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>SciTech Premium Collection</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Earth and space science (Hoboken, N.J.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rongier, Guillaume</au><au>Rude, Cody</au><au>Herring, Thomas</au><au>Pankratius, Victor</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generative Modeling of InSAR Interferograms</atitle><jtitle>Earth and space science (Hoboken, N.J.)</jtitle><addtitle>Earth Space Sci</addtitle><date>2019-12</date><risdate>2019</risdate><volume>6</volume><issue>12</issue><spage>2671</spage><epage>2683</epage><pages>2671-2683</pages><issn>2333-5084</issn><eissn>2333-5084</eissn><abstract>Interferometric synthetic aperture radar (InSAR) has become an essential technique to detect surface variations due to volcanoes, earthquakes, landslides, glaciers, and aquifers. However, Earth's ionosphere, atmosphere, vegetation, surface runoff, etc., introduce noise that requires post‐processing to separate its components. This work defines a generator to create interferograms that include each of those components. Our approach leverages deformation models with real data, either directly or through machine learning using geostatistical methods. These methods result from previous developments to more efficiently and better simulate spatial variables and could replace some statistical approaches used in InSAR processing. We illustrate the use of the generator to simulate an artificial interferogram based on the 2015 Illapel earthquake and discuss the improved performance offered by geostatistical approaches compared with classical statistical ones. The generator establishes a tool for multiple applications (1) to evaluate InSAR correction workflows in controlled scenarios with known ground truth; (2) to develop training sets and generative methods for machine learning algorithms; and (3) to educate on InSAR and its principles.
Key Points
We introduce a software tool that can generate artificial interferograms for synthetic aperture radar (SAR) applications
The tool leverages real data and geostatistical methods to generate and perturb interferogram components
It can be used to evaluate InSAR error correction workflows, to enhance machine learning use with InSAR, and to teach InSAR principles</abstract><cop>United States</cop><pub>John Wiley & Sons, Inc</pub><pmid>32064305</pmid><doi>10.1029/2018EA000533</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-4658-6583</orcidid><orcidid>https://orcid.org/0000-0002-5910-6868</orcidid><orcidid>https://orcid.org/0000-0002-6030-0545</orcidid><orcidid>https://orcid.org/0000-0002-9584-2600</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2333-5084 |
ispartof | Earth and space science (Hoboken, N.J.), 2019-12, Vol.6 (12), p.2671-2683 |
issn | 2333-5084 2333-5084 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_e4c58809c7c1413184554cff31d42cd9 |
source | Wiley_OA刊; Publicly Available Content (ProQuest) |
subjects | Algorithms Avalanches Cryosphere Deformation Earthquake Ground Motions and Engineering Seismology Earthquakes Effusive Volcanism Error correction & detection Explosive Volcanism generator Geodesy and Gravity Geological geostatistics Glaciers Glaciohydrology Informatics InSAR Instruments and Techniques Ionosphere Landslides Machine learning Mathematical Geophysics Mud Volcanism Natural Hazards Noise Oceanography: Physical Seismic activity Seismology surface deformation Surface runoff Technical Reports: Methods Topography Tsunamis and Storm Surges Values Volcanic Hazards and Risks Volcano Monitoring Volcano Seismology Volcanoes Volcanology Workflow |
title | Generative Modeling of InSAR Interferograms |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T06%3A17%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Generative%20Modeling%20of%20InSAR%20Interferograms&rft.jtitle=Earth%20and%20space%20science%20(Hoboken,%20N.J.)&rft.au=Rongier,%20Guillaume&rft.date=2019-12&rft.volume=6&rft.issue=12&rft.spage=2671&rft.epage=2683&rft.pages=2671-2683&rft.issn=2333-5084&rft.eissn=2333-5084&rft_id=info:doi/10.1029/2018EA000533&rft_dat=%3Cproquest_doaj_%3E2344184918%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a5432-f587c7d16e4bc798102d0719ef54f3c3ae7d4f44c1a3b039218075f216b62a753%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2344184918&rft_id=info:pmid/32064305&rfr_iscdi=true |