Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation

SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are desi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Geophysical journal international 2020-11, Vol.223 (2), p.811-824
Hauptverfasser: Huang, Chao, Zhu, Tieyuan
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 824
container_issue 2
container_start_page 811
container_title Geophysical journal international
container_volume 223
creator Huang, Chao
Zhu, Tieyuan
description SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here, we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence, HiEKF is computationally efficient and applicable to 3-D TLFWI problems. Then, we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI–HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2-D and 3-D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.
doi_str_mv 10.1093/gji/ggaa337
format Article
fullrecord <record><control><sourceid>oup_TOX</sourceid><recordid>TN_cdi_osti_scitechconnect_1635795</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/gji/ggaa337</oup_id><sourcerecordid>10.1093/gji/ggaa337</sourcerecordid><originalsourceid>FETCH-LOGICAL-a351t-6b77386f8cfed111cb527c75cba6398bdbe25fb67fa36f50781a83f4e803ec0b3</originalsourceid><addsrcrecordid>eNp9kEtLAzEUhYMoWB8r_0Bw4UZGk2aSTN2J-ALBTYXuhjuZmxqZSUqSVrr0nxtp164uBz6-eziEXHB2w9lM3C6_3O1yCSCEPiATLpSsprVaHJIJm0lVyZotjslJSl-M8ZrXzYT8zMM3xD7RiDBU2Y1Ix-BdDtH55R3tIQOFlNzoBsjY0z-iGmCVkNr1MNBv2KANcaTObzAmFzwtkSZ0aXSGbnAIxuUtBd_TtTcYMzhfMqZiglz4M3JkYUh4vr-n5OPpcf7wUr29P78-3L9VICTPleq0Fo2yjbHYc85NJ6faaGk6UGLWdH2HU2k7pS0IZSXTDYdG2BobJtCwTpySy503lNdtKq3QfJrgPZrcciWknskCXe8gE0NKEW27iqVn3LactX8Tt2Xidj9xoa_2yvXqX_AXZKCBlQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation</title><source>Oxford Journals Open Access Collection</source><creator>Huang, Chao ; Zhu, Tieyuan</creator><creatorcontrib>Huang, Chao ; Zhu, Tieyuan ; Pennsylvania State Univ., University Park, PA (United States)</creatorcontrib><description>SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here, we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence, HiEKF is computationally efficient and applicable to 3-D TLFWI problems. Then, we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI–HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2-D and 3-D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.</description><identifier>ISSN: 0956-540X</identifier><identifier>EISSN: 1365-246X</identifier><identifier>DOI: 10.1093/gji/ggaa337</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Carbon storage and sequestration ; GEOSCIENCES ; inverse theory ; probability forecasting ; waveform inversion</subject><ispartof>Geophysical journal international, 2020-11, Vol.223 (2), p.811-824</ispartof><rights>The Author(s) 2020. Published by Oxford University Press on behalf of The Royal Astronomical Society. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a351t-6b77386f8cfed111cb527c75cba6398bdbe25fb67fa36f50781a83f4e803ec0b3</citedby><cites>FETCH-LOGICAL-a351t-6b77386f8cfed111cb527c75cba6398bdbe25fb67fa36f50781a83f4e803ec0b3</cites><orcidid>0000-0003-0800-7429 ; 0000000331728240 ; 0000000308007429</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,1604,27924,27925</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/gji/ggaa337$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.osti.gov/biblio/1635795$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Huang, Chao</creatorcontrib><creatorcontrib>Zhu, Tieyuan</creatorcontrib><creatorcontrib>Pennsylvania State Univ., University Park, PA (United States)</creatorcontrib><title>Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation</title><title>Geophysical journal international</title><description>SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here, we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence, HiEKF is computationally efficient and applicable to 3-D TLFWI problems. Then, we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI–HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2-D and 3-D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.</description><subject>Carbon storage and sequestration</subject><subject>GEOSCIENCES</subject><subject>inverse theory</subject><subject>probability forecasting</subject><subject>waveform inversion</subject><issn>0956-540X</issn><issn>1365-246X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLAzEUhYMoWB8r_0Bw4UZGk2aSTN2J-ALBTYXuhjuZmxqZSUqSVrr0nxtp164uBz6-eziEXHB2w9lM3C6_3O1yCSCEPiATLpSsprVaHJIJm0lVyZotjslJSl-M8ZrXzYT8zMM3xD7RiDBU2Y1Ix-BdDtH55R3tIQOFlNzoBsjY0z-iGmCVkNr1MNBv2KANcaTObzAmFzwtkSZ0aXSGbnAIxuUtBd_TtTcYMzhfMqZiglz4M3JkYUh4vr-n5OPpcf7wUr29P78-3L9VICTPleq0Fo2yjbHYc85NJ6faaGk6UGLWdH2HU2k7pS0IZSXTDYdG2BobJtCwTpySy503lNdtKq3QfJrgPZrcciWknskCXe8gE0NKEW27iqVn3LactX8Tt2Xidj9xoa_2yvXqX_AXZKCBlQ</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Huang, Chao</creator><creator>Zhu, Tieyuan</creator><general>Oxford University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>OTOTI</scope><orcidid>https://orcid.org/0000-0003-0800-7429</orcidid><orcidid>https://orcid.org/0000000331728240</orcidid><orcidid>https://orcid.org/0000000308007429</orcidid></search><sort><creationdate>20201101</creationdate><title>Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation</title><author>Huang, Chao ; Zhu, Tieyuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a351t-6b77386f8cfed111cb527c75cba6398bdbe25fb67fa36f50781a83f4e803ec0b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Carbon storage and sequestration</topic><topic>GEOSCIENCES</topic><topic>inverse theory</topic><topic>probability forecasting</topic><topic>waveform inversion</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Chao</creatorcontrib><creatorcontrib>Zhu, Tieyuan</creatorcontrib><creatorcontrib>Pennsylvania State Univ., University Park, PA (United States)</creatorcontrib><collection>CrossRef</collection><collection>OSTI.GOV</collection><jtitle>Geophysical journal international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Huang, Chao</au><au>Zhu, Tieyuan</au><aucorp>Pennsylvania State Univ., University Park, PA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation</atitle><jtitle>Geophysical journal international</jtitle><date>2020-11-01</date><risdate>2020</risdate><volume>223</volume><issue>2</issue><spage>811</spage><epage>824</epage><pages>811-824</pages><issn>0956-540X</issn><eissn>1365-246X</eissn><abstract>SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here, we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence, HiEKF is computationally efficient and applicable to 3-D TLFWI problems. Then, we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI–HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2-D and 3-D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.</abstract><cop>United States</cop><pub>Oxford University Press</pub><doi>10.1093/gji/ggaa337</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-0800-7429</orcidid><orcidid>https://orcid.org/0000000331728240</orcidid><orcidid>https://orcid.org/0000000308007429</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 0956-540X
ispartof Geophysical journal international, 2020-11, Vol.223 (2), p.811-824
issn 0956-540X
1365-246X
language eng
recordid cdi_osti_scitechconnect_1635795
source Oxford Journals Open Access Collection
subjects Carbon storage and sequestration
GEOSCIENCES
inverse theory
probability forecasting
waveform inversion
title Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T09%3A15%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-oup_TOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Towards%20real-time%20monitoring:%20data%20assimilated%20time-lapse%20full%20waveform%20inversion%20for%20seismic%20velocity%20and%20uncertainty%20estimation&rft.jtitle=Geophysical%20journal%20international&rft.au=Huang,%20Chao&rft.aucorp=Pennsylvania%20State%20Univ.,%20University%20Park,%20PA%20(United%20States)&rft.date=2020-11-01&rft.volume=223&rft.issue=2&rft.spage=811&rft.epage=824&rft.pages=811-824&rft.issn=0956-540X&rft.eissn=1365-246X&rft_id=info:doi/10.1093/gji/ggaa337&rft_dat=%3Coup_TOX%3E10.1093/gji/ggaa337%3C/oup_TOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_oup_id=10.1093/gji/ggaa337&rfr_iscdi=true