Marginalized iterative ensemble smoothers for data assimilation
Data assimilation is an important tool in many geophysical applications. One of many key elements of data assimilation algorithms is the measurement error that determines the weighting of the data in the cost function to be minimized. Although the algorithms used for data assimilation treat the meas...
Gespeichert in:
| Veröffentlicht in: | Computational geosciences 2023-12, Vol.27 (6), p.975-986 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 986 |
|---|---|
| container_issue | 6 |
| container_start_page | 975 |
| container_title | Computational geosciences |
| container_volume | 27 |
| creator | Stordal, Andreas S. Lorentzen, Rolf J. Fossum, Kristian |
| description | Data assimilation is an important tool in many geophysical applications. One of many key elements of data assimilation algorithms is the measurement error that determines the weighting of the data in the cost function to be minimized. Although the algorithms used for data assimilation treat the measurement uncertainty as known, it is in many cases estimated or set based on some expert opinion. Here we treat the measurement uncertainty as a hyperparameter in a fully Bayesian hierarchical model and derive a new class of iterative ensemble methods for data assimilation where the measurement uncertainty is integrated out. The proposed algorithms are compared with the standard iterative ensemble smoother on a 2D synthetic reservoir model. |
| doi_str_mv | 10.1007/s10596-023-10242-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2898158013</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2898158013</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-79a7d8e09a69586d7388193226515438decc7409f57ea6e930da42c16200cf293</originalsourceid><addsrcrecordid>eNp9kE1LxDAQhoMouK7-AU8Fz9FJ0nydRBa_YMWLnkNsp2uWtlmTrqC_3ugK3jzNDDzvy_AQcsrgnAHoi8xAWkWBC8qA15yyPTJjUpeztna_7DUHWhh9SI5yXgOA1YLNyOWDT6sw-j58YluFCZOfwjtWOGYcXnqs8hDj9IopV11MVesnX_mcwxD6AsbxmBx0vs948jvn5Pnm-mlxR5ePt_eLqyVthBIT1dbr1iBYr6w0qtXCGGYF50oyWQvTYtPoGmwnNXqFVkDra94wxQGajlsxJ2e73k2Kb1vMk1vHbSp_Z8eNNUwaYKJQfEc1KeacsHObFAafPhwD9y3K7US5Isr9iHKshMQulAs8rjD9Vf-T-gJlVGpH</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2898158013</pqid></control><display><type>article</type><title>Marginalized iterative ensemble smoothers for data assimilation</title><source>SpringerLink</source><creator>Stordal, Andreas S. ; Lorentzen, Rolf J. ; Fossum, Kristian</creator><creatorcontrib>Stordal, Andreas S. ; Lorentzen, Rolf J. ; Fossum, Kristian</creatorcontrib><description>Data assimilation is an important tool in many geophysical applications. One of many key elements of data assimilation algorithms is the measurement error that determines the weighting of the data in the cost function to be minimized. Although the algorithms used for data assimilation treat the measurement uncertainty as known, it is in many cases estimated or set based on some expert opinion. Here we treat the measurement uncertainty as a hyperparameter in a fully Bayesian hierarchical model and derive a new class of iterative ensemble methods for data assimilation where the measurement uncertainty is integrated out. The proposed algorithms are compared with the standard iterative ensemble smoother on a 2D synthetic reservoir model.</description><identifier>ISSN: 1420-0597</identifier><identifier>EISSN: 1573-1499</identifier><identifier>DOI: 10.1007/s10596-023-10242-1</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Algorithms ; Bayesian analysis ; Cost function ; Data assimilation ; Data collection ; Earth and Environmental Science ; Earth Sciences ; Error analysis ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Inverse problems ; Iterative methods ; Mathematical Modeling and Industrial Mathematics ; New class ; Original Paper ; Parameter estimation ; Probability theory ; Random variables ; Soil Science & Conservation ; Uncertainty</subject><ispartof>Computational geosciences, 2023-12, Vol.27 (6), p.975-986</ispartof><rights>The Author(s) 2023</rights><rights>The Author(s) 2023. 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-c363t-79a7d8e09a69586d7388193226515438decc7409f57ea6e930da42c16200cf293</citedby><cites>FETCH-LOGICAL-c363t-79a7d8e09a69586d7388193226515438decc7409f57ea6e930da42c16200cf293</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10596-023-10242-1$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10596-023-10242-1$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids></links><search><creatorcontrib>Stordal, Andreas S.</creatorcontrib><creatorcontrib>Lorentzen, Rolf J.</creatorcontrib><creatorcontrib>Fossum, Kristian</creatorcontrib><title>Marginalized iterative ensemble smoothers for data assimilation</title><title>Computational geosciences</title><addtitle>Comput Geosci</addtitle><description>Data assimilation is an important tool in many geophysical applications. One of many key elements of data assimilation algorithms is the measurement error that determines the weighting of the data in the cost function to be minimized. Although the algorithms used for data assimilation treat the measurement uncertainty as known, it is in many cases estimated or set based on some expert opinion. Here we treat the measurement uncertainty as a hyperparameter in a fully Bayesian hierarchical model and derive a new class of iterative ensemble methods for data assimilation where the measurement uncertainty is integrated out. The proposed algorithms are compared with the standard iterative ensemble smoother on a 2D synthetic reservoir model.</description><subject>Algorithms</subject><subject>Bayesian analysis</subject><subject>Cost function</subject><subject>Data assimilation</subject><subject>Data collection</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Error analysis</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Inverse problems</subject><subject>Iterative methods</subject><subject>Mathematical Modeling and Industrial Mathematics</subject><subject>New class</subject><subject>Original Paper</subject><subject>Parameter estimation</subject><subject>Probability theory</subject><subject>Random variables</subject><subject>Soil Science & Conservation</subject><subject>Uncertainty</subject><issn>1420-0597</issn><issn>1573-1499</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE1LxDAQhoMouK7-AU8Fz9FJ0nydRBa_YMWLnkNsp2uWtlmTrqC_3ugK3jzNDDzvy_AQcsrgnAHoi8xAWkWBC8qA15yyPTJjUpeztna_7DUHWhh9SI5yXgOA1YLNyOWDT6sw-j58YluFCZOfwjtWOGYcXnqs8hDj9IopV11MVesnX_mcwxD6AsbxmBx0vs948jvn5Pnm-mlxR5ePt_eLqyVthBIT1dbr1iBYr6w0qtXCGGYF50oyWQvTYtPoGmwnNXqFVkDra94wxQGajlsxJ2e73k2Kb1vMk1vHbSp_Z8eNNUwaYKJQfEc1KeacsHObFAafPhwD9y3K7US5Isr9iHKshMQulAs8rjD9Vf-T-gJlVGpH</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Stordal, Andreas S.</creator><creator>Lorentzen, Rolf J.</creator><creator>Fossum, Kristian</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L.G</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M2P</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>20231201</creationdate><title>Marginalized iterative ensemble smoothers for data assimilation</title><author>Stordal, Andreas S. ; Lorentzen, Rolf J. ; Fossum, Kristian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-79a7d8e09a69586d7388193226515438decc7409f57ea6e930da42c16200cf293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Bayesian analysis</topic><topic>Cost function</topic><topic>Data assimilation</topic><topic>Data collection</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Error analysis</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Hydrogeology</topic><topic>Inverse problems</topic><topic>Iterative methods</topic><topic>Mathematical Modeling and Industrial Mathematics</topic><topic>New class</topic><topic>Original Paper</topic><topic>Parameter estimation</topic><topic>Probability theory</topic><topic>Random variables</topic><topic>Soil Science & Conservation</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Stordal, Andreas S.</creatorcontrib><creatorcontrib>Lorentzen, Rolf J.</creatorcontrib><creatorcontrib>Fossum, Kristian</creatorcontrib><collection>Springer Open Access</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (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>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Database (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>ProQuest Science Journals</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Earth, Atmospheric & Aquatic Science 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 Basic</collection><jtitle>Computational geosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Stordal, Andreas S.</au><au>Lorentzen, Rolf J.</au><au>Fossum, Kristian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Marginalized iterative ensemble smoothers for data assimilation</atitle><jtitle>Computational geosciences</jtitle><stitle>Comput Geosci</stitle><date>2023-12-01</date><risdate>2023</risdate><volume>27</volume><issue>6</issue><spage>975</spage><epage>986</epage><pages>975-986</pages><issn>1420-0597</issn><eissn>1573-1499</eissn><abstract>Data assimilation is an important tool in many geophysical applications. One of many key elements of data assimilation algorithms is the measurement error that determines the weighting of the data in the cost function to be minimized. Although the algorithms used for data assimilation treat the measurement uncertainty as known, it is in many cases estimated or set based on some expert opinion. Here we treat the measurement uncertainty as a hyperparameter in a fully Bayesian hierarchical model and derive a new class of iterative ensemble methods for data assimilation where the measurement uncertainty is integrated out. The proposed algorithms are compared with the standard iterative ensemble smoother on a 2D synthetic reservoir model.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s10596-023-10242-1</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1420-0597 |
| ispartof | Computational geosciences, 2023-12, Vol.27 (6), p.975-986 |
| issn | 1420-0597 1573-1499 |
| language | eng |
| recordid | cdi_proquest_journals_2898158013 |
| source | SpringerLink |
| subjects | Algorithms Bayesian analysis Cost function Data assimilation Data collection Earth and Environmental Science Earth Sciences Error analysis Geotechnical Engineering & Applied Earth Sciences Hydrogeology Inverse problems Iterative methods Mathematical Modeling and Industrial Mathematics New class Original Paper Parameter estimation Probability theory Random variables Soil Science & Conservation Uncertainty |
| title | Marginalized iterative ensemble smoothers for data assimilation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T14%3A46%3A01IST&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=Marginalized%20iterative%20ensemble%20smoothers%20for%20data%20assimilation&rft.jtitle=Computational%20geosciences&rft.au=Stordal,%20Andreas%20S.&rft.date=2023-12-01&rft.volume=27&rft.issue=6&rft.spage=975&rft.epage=986&rft.pages=975-986&rft.issn=1420-0597&rft.eissn=1573-1499&rft_id=info:doi/10.1007/s10596-023-10242-1&rft_dat=%3Cproquest_cross%3E2898158013%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2898158013&rft_id=info:pmid/&rfr_iscdi=true |