Large-scale inverse modeling with an application in hydraulic tomography

Inverse modeling has been widely used in subsurface problems, where direct measurements of parameters are expensive and sometimes impossible. Subsurface media are inherently heterogeneous in complex ways, which implies that the number of unknowns is usually large. Furthermore, technologies such as h...

Full description

Saved in:
Bibliographic Details
Published in:Water resources research 2011-02, Vol.47 (2), p.n/a
Main Authors: Liu, X., Kitanidis, P. K.
Format: Article
Language:English
Subjects:
Algorithms
Fourier transforms
Geophysics
geostatistics
Groundwater
High performance computing
hydraulic tomography
Hydraulics
Hydrology
inverse modeling
Iterative methods
large-scale
least square
Mathematics
Methods
Sparsity
Tomography
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-a4051-2c1844d6366223e8a958e3a4c5593c1a09d1c213d3a2ea381d2e584e7d52cfb33
cites cdi_FETCH-LOGICAL-a4051-2c1844d6366223e8a958e3a4c5593c1a09d1c213d3a2ea381d2e584e7d52cfb33
container_end_page n/a
container_issue 2
container_start_page
container_title Water resources research
container_volume 47
creator Liu, X.
Kitanidis, P. K.
description Inverse modeling has been widely used in subsurface problems, where direct measurements of parameters are expensive and sometimes impossible. Subsurface media are inherently heterogeneous in complex ways, which implies that the number of unknowns is usually large. Furthermore, technologies such as hydraulic tomography and electric resistivity tomography allow the collection of more indirect measurements, and at the same time, there is an increased appreciation of the value of detailed characterization of the subsurface media in, for example, remediation projects. Hence, we need efficient inverse methods that can assimilate a large volume of measurements to estimate even larger numbers of parameters, i.e., large‐scale inverse modeling. In this paper, we present a Bayesian method that employs a sparse formulation, and we applied this method to a laboratory hydraulic tomography problem, where we successfully estimated half a million unknowns that represent the hydraulic conductivity field of the sandbox at a fine scale. The inversion took about 2 h with a single core.
doi_str_mv 10.1029/2010WR009144
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_862824621</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2325723171</sourcerecordid><originalsourceid>FETCH-LOGICAL-a4051-2c1844d6366223e8a958e3a4c5593c1a09d1c213d3a2ea381d2e584e7d52cfb33</originalsourceid><addsrcrecordid>eNp9kE1Lw0AURQdRsFZ3_oDg2uh8J7OUqq1QqxQl4mZ4Tqbt1DSJM6k1_96Uirhy9eBxzr1wETol-IJgqi4pJjibYqwI53uoRxTncaISto96GHMWE6aSQ3QUwhJjwoVMemg0Bj-3cTBQ2MiVn9YHG62q3BaunEcb1ywiKCOo68IZaFxVdlC0aHMP6-4TNdWqmnuoF-0xOphBEezJz-2j59ubp8EoHj8M7wZX4xg4FiSmhqSc55JJSSmzKSiRWgbcCKGYIYBVTgwlLGdALbCU5NSKlNskF9TM3hjro7Ndbu2rj7UNjV5Wa192lTqVNKVcdnYfne8g46sQvJ3p2rsV-FYTrLdT6b9TdTjb4RtX2PZfVmfTwZRQybcl8c5yobFfvxb4dy0TlgidTYZ6cv-a0evHFy3ZNzvweQc</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>862824621</pqid></control><display><type>article</type><title>Large-scale inverse modeling with an application in hydraulic tomography</title><source>ABI/INFORM global</source><source>Wiley Online Library AGU Backfiles</source><creator>Liu, X. ; Kitanidis, P. K.</creator><creatorcontrib>Liu, X. ; Kitanidis, P. K.</creatorcontrib><description>Inverse modeling has been widely used in subsurface problems, where direct measurements of parameters are expensive and sometimes impossible. Subsurface media are inherently heterogeneous in complex ways, which implies that the number of unknowns is usually large. Furthermore, technologies such as hydraulic tomography and electric resistivity tomography allow the collection of more indirect measurements, and at the same time, there is an increased appreciation of the value of detailed characterization of the subsurface media in, for example, remediation projects. Hence, we need efficient inverse methods that can assimilate a large volume of measurements to estimate even larger numbers of parameters, i.e., large‐scale inverse modeling. In this paper, we present a Bayesian method that employs a sparse formulation, and we applied this method to a laboratory hydraulic tomography problem, where we successfully estimated half a million unknowns that represent the hydraulic conductivity field of the sandbox at a fine scale. The inversion took about 2 h with a single core.</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2010WR009144</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Fourier transforms ; Geophysics ; geostatistics ; Groundwater ; High performance computing ; hydraulic tomography ; Hydraulics ; Hydrology ; inverse modeling ; Iterative methods ; large-scale ; least square ; Mathematics ; Methods ; Sparsity ; Tomography</subject><ispartof>Water resources research, 2011-02, Vol.47 (2), p.n/a</ispartof><rights>Copyright 2011 by the American Geophysical Union.</rights><rights>Copyright 2011 by American Geophysical Union</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a4051-2c1844d6366223e8a958e3a4c5593c1a09d1c213d3a2ea381d2e584e7d52cfb33</citedby><cites>FETCH-LOGICAL-a4051-2c1844d6366223e8a958e3a4c5593c1a09d1c213d3a2ea381d2e584e7d52cfb33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/862824621/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/862824621?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11514,11688,27924,27925,36060,44363,46468,46892,74895</link.rule.ids></links><search><creatorcontrib>Liu, X.</creatorcontrib><creatorcontrib>Kitanidis, P. K.</creatorcontrib><title>Large-scale inverse modeling with an application in hydraulic tomography</title><title>Water resources research</title><addtitle>Water Resour. Res</addtitle><description>Inverse modeling has been widely used in subsurface problems, where direct measurements of parameters are expensive and sometimes impossible. Subsurface media are inherently heterogeneous in complex ways, which implies that the number of unknowns is usually large. Furthermore, technologies such as hydraulic tomography and electric resistivity tomography allow the collection of more indirect measurements, and at the same time, there is an increased appreciation of the value of detailed characterization of the subsurface media in, for example, remediation projects. Hence, we need efficient inverse methods that can assimilate a large volume of measurements to estimate even larger numbers of parameters, i.e., large‐scale inverse modeling. In this paper, we present a Bayesian method that employs a sparse formulation, and we applied this method to a laboratory hydraulic tomography problem, where we successfully estimated half a million unknowns that represent the hydraulic conductivity field of the sandbox at a fine scale. The inversion took about 2 h with a single core.</description><subject>Algorithms</subject><subject>Fourier transforms</subject><subject>Geophysics</subject><subject>geostatistics</subject><subject>Groundwater</subject><subject>High performance computing</subject><subject>hydraulic tomography</subject><subject>Hydraulics</subject><subject>Hydrology</subject><subject>inverse modeling</subject><subject>Iterative methods</subject><subject>large-scale</subject><subject>least square</subject><subject>Mathematics</subject><subject>Methods</subject><subject>Sparsity</subject><subject>Tomography</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp9kE1Lw0AURQdRsFZ3_oDg2uh8J7OUqq1QqxQl4mZ4Tqbt1DSJM6k1_96Uirhy9eBxzr1wETol-IJgqi4pJjibYqwI53uoRxTncaISto96GHMWE6aSQ3QUwhJjwoVMemg0Bj-3cTBQ2MiVn9YHG62q3BaunEcb1ywiKCOo68IZaFxVdlC0aHMP6-4TNdWqmnuoF-0xOphBEezJz-2j59ubp8EoHj8M7wZX4xg4FiSmhqSc55JJSSmzKSiRWgbcCKGYIYBVTgwlLGdALbCU5NSKlNskF9TM3hjro7Ndbu2rj7UNjV5Wa192lTqVNKVcdnYfne8g46sQvJ3p2rsV-FYTrLdT6b9TdTjb4RtX2PZfVmfTwZRQybcl8c5yobFfvxb4dy0TlgidTYZ6cv-a0evHFy3ZNzvweQc</recordid><startdate>201102</startdate><enddate>201102</enddate><creator>Liu, X.</creator><creator>Kitanidis, P. K.</creator><general>Blackwell Publishing Ltd</general><general>John Wiley &amp; Sons, Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>H96</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>KL.</scope><scope>KR7</scope><scope>L.-</scope><scope>L.G</scope><scope>L6V</scope><scope>M0C</scope><scope>M2O</scope><scope>M7N</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P64</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope></search><sort><creationdate>201102</creationdate><title>Large-scale inverse modeling with an application in hydraulic tomography</title><author>Liu, X. ; Kitanidis, P. K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a4051-2c1844d6366223e8a958e3a4c5593c1a09d1c213d3a2ea381d2e584e7d52cfb33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Fourier transforms</topic><topic>Geophysics</topic><topic>geostatistics</topic><topic>Groundwater</topic><topic>High performance computing</topic><topic>hydraulic tomography</topic><topic>Hydraulics</topic><topic>Hydrology</topic><topic>inverse modeling</topic><topic>Iterative methods</topic><topic>large-scale</topic><topic>least square</topic><topic>Mathematics</topic><topic>Methods</topic><topic>Sparsity</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, X.</creatorcontrib><creatorcontrib>Kitanidis, P. K.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (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>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric &amp; 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>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM global</collection><collection>ProQuest research library</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, X.</au><au>Kitanidis, P. K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Large-scale inverse modeling with an application in hydraulic tomography</atitle><jtitle>Water resources research</jtitle><addtitle>Water Resour. Res</addtitle><date>2011-02</date><risdate>2011</risdate><volume>47</volume><issue>2</issue><epage>n/a</epage><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>Inverse modeling has been widely used in subsurface problems, where direct measurements of parameters are expensive and sometimes impossible. Subsurface media are inherently heterogeneous in complex ways, which implies that the number of unknowns is usually large. Furthermore, technologies such as hydraulic tomography and electric resistivity tomography allow the collection of more indirect measurements, and at the same time, there is an increased appreciation of the value of detailed characterization of the subsurface media in, for example, remediation projects. Hence, we need efficient inverse methods that can assimilate a large volume of measurements to estimate even larger numbers of parameters, i.e., large‐scale inverse modeling. In this paper, we present a Bayesian method that employs a sparse formulation, and we applied this method to a laboratory hydraulic tomography problem, where we successfully estimated half a million unknowns that represent the hydraulic conductivity field of the sandbox at a fine scale. The inversion took about 2 h with a single core.</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2010WR009144</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0043-1397
ispartof Water resources research, 2011-02, Vol.47 (2), p.n/a
issn 0043-1397
1944-7973
language eng
recordid cdi_proquest_journals_862824621
source ABI/INFORM global; Wiley Online Library AGU Backfiles
subjects Algorithms
Fourier transforms
Geophysics
geostatistics
Groundwater
High performance computing
hydraulic tomography
Hydraulics
Hydrology
inverse modeling
Iterative methods
large-scale
least square
Mathematics
Methods
Sparsity
Tomography
title Large-scale inverse modeling with an application in hydraulic tomography
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T09%3A38%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Large-scale%20inverse%20modeling%20with%20an%20application%20in%20hydraulic%20tomography&rft.jtitle=Water%20resources%20research&rft.au=Liu,%20X.&rft.date=2011-02&rft.volume=47&rft.issue=2&rft.epage=n/a&rft.issn=0043-1397&rft.eissn=1944-7973&rft_id=info:doi/10.1029/2010WR009144&rft_dat=%3Cproquest_cross%3E2325723171%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a4051-2c1844d6366223e8a958e3a4c5593c1a09d1c213d3a2ea381d2e584e7d52cfb33%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=862824621&rft_id=info:pmid/&rfr_iscdi=true