Operator‐oriented CRS interpolation
Identifieur interne : 001257 ( Istex/Corpus ); précédent : 001256; suivant : 001258Operator‐oriented CRS interpolation
Auteurs : German Hoecht ; Patrice Ricarte ; Steffen Bergler ; Evgeny LandaSource :
- Geophysical Prospecting [ 0016-8025 ] ; 2009-11.
Abstract
In common‐reflection‐surface imaging the reflection arrival time field is parameterized by operators that are of higher dimension or order than in conventional methods. Using the common‐reflection‐surface approach locally in the unmigrated prestack data domain opens a potential for trace regularization and interpolation. In most data interpolation methods based on local coherency estimation, a single operator is designed for a target sample and the output amplitude is defined as a weighted average along the operator. This approach may fail in presence of interfering events or strong amplitude and phase variations. In this paper we introduce an alternative scheme in which there is no need for an operator to be defined at the target sample itself. Instead, the amplitude at a target sample is constructed from multiple operators estimated at different positions. In this case one operator may contribute to the construction of several target samples. Vice versa, a target sample might receive contributions from different operators. Operators are determined on a grid which can be sparser than the output grid. This allows to dramatically decrease the computational costs. In addition, the use of multiple operators for a single target sample stabilizes the interpolation results and implicitly allows several contributions in case of interfering events. Due to the considerable computational expense, common‐reflection‐surface interpolation is limited to work in subsets of the prestack data. We present the general workflow of a common‐reflection‐surface‐based regularization/interpolation for 3D data volumes. This workflow has been applied to an OBC common‐receiver volume and binned common‐offset subsets of a 3D marine data set. The impact of a common‐reflection‐surface regularization is demonstrated by means of a subsequent time migration. In comparison to the time migrations of the original and DMO‐interpolated data, the results show particular improvements in view of the continuity of reflections events. This gain is confirmed by an automatic picking of a horizon in the stacked time migrations.
Url:
DOI: 10.1111/j.1365-2478.2009.00789.x
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ISTEX:0179D900EEEF9035D8481E48D84BD170DC18F484Le document en format XML
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Using the common‐reflection‐surface approach locally in the unmigrated prestack data domain opens a potential for trace regularization and interpolation. In most data interpolation methods based on local coherency estimation, a single operator is designed for a target sample and the output amplitude is defined as a weighted average along the operator. This approach may fail in presence of interfering events or strong amplitude and phase variations. In this paper we introduce an alternative scheme in which there is no need for an operator to be defined at the target sample itself. Instead, the amplitude at a target sample is constructed from multiple operators estimated at different positions. In this case one operator may contribute to the construction of several target samples. Vice versa, a target sample might receive contributions from different operators. Operators are determined on a grid which can be sparser than the output grid. This allows to dramatically decrease the computational costs. In addition, the use of multiple operators for a single target sample stabilizes the interpolation results and implicitly allows several contributions in case of interfering events. Due to the considerable computational expense, common‐reflection‐surface interpolation is limited to work in subsets of the prestack data. We present the general workflow of a common‐reflection‐surface‐based regularization/interpolation for 3D data volumes. This workflow has been applied to an OBC common‐receiver volume and binned common‐offset subsets of a 3D marine data set. The impact of a common‐reflection‐surface regularization is demonstrated by means of a subsequent time migration. In comparison to the time migrations of the original and DMO‐interpolated data, the results show particular improvements in view of the continuity of reflections events. This gain is confirmed by an automatic picking of a horizon in the stacked time migrations.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>German Hoecht</name><affiliations><json:string>OPERA, Bâtiment IFR, rue Jules Ferry, 64000 Pau, France</json:string></affiliations></json:item><json:item><name>Patrice Ricarte</name><affiliations><json:string>Institut Français du Pétrole (IFP), 1‐4 avenue de Bois‐Preau, 92852 Ruiel Malmaison Cedex, France</json:string></affiliations></json:item><json:item><name>Steffen Bergler</name><affiliations><json:string>Shell International Exploration & Production, Kessler Park 1, 2288 GS Rijswijk, The Netherlands</json:string></affiliations></json:item><json:item><name>Evgeny Landa</name><affiliations><json:string>OPERA, Bâtiment IFR, rue Jules Ferry, 64000 Pau, France</json:string></affiliations></json:item></author><articleId><json:string>GPR789</json:string></articleId><language><json:string>eng</json:string></language><abstract>In common‐reflection‐surface imaging the reflection arrival time field is parameterized by operators that are of higher dimension or order than in conventional methods. 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This allows to dramatically decrease the computational costs. In addition, the use of multiple operators for a single target sample stabilizes the interpolation results and implicitly allows several contributions in case of interfering events. Due to the considerable computational expense, common‐reflection‐surface interpolation is limited to work in subsets of the prestack data. We present the general workflow of a common‐reflection‐surface‐based regularization/interpolation for 3D data volumes. This workflow has been applied to an OBC common‐receiver volume and binned common‐offset subsets of a 3D marine data set. The impact of a common‐reflection‐surface regularization is demonstrated by means of a subsequent time migration. In comparison to the time migrations of the original and DMO‐interpolated data, the results show particular improvements in view of the continuity of reflections events. 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Using the common‐reflection‐surface approach locally in the unmigrated prestack data domain opens a potential for trace regularization and interpolation. In most data interpolation methods based on local coherency estimation, a single operator is designed for a target sample and the output amplitude is defined as a weighted average along the operator. This approach may fail in presence of interfering events or strong amplitude and phase variations. In this paper we introduce an alternative scheme in which there is no need for an operator to be defined at the target sample itself. Instead, the amplitude at a target sample is constructed from multiple operators estimated at different positions. In this case one operator may contribute to the construction of several target samples. Vice versa, a target sample might receive contributions from different operators. Operators are determined on a grid which can be sparser than the output grid. This allows to dramatically decrease the computational costs. In addition, the use of multiple operators for a single target sample stabilizes the interpolation results and implicitly allows several contributions in case of interfering events. Due to the considerable computational expense, common‐reflection‐surface interpolation is limited to work in subsets of the prestack data. We present the general workflow of a common‐reflection‐surface‐based regularization/interpolation for 3D data volumes. This workflow has been applied to an OBC common‐receiver volume and binned common‐offset subsets of a 3D marine data set. The impact of a common‐reflection‐surface regularization is demonstrated by means of a subsequent time migration. In comparison to the time migrations of the original and DMO‐interpolated data, the results show particular improvements in view of the continuity of reflections events. 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Hoecht</i> et al.</title><title type="short"><i>Common‐reflection‐surface‐based interpolation</i></title></titleGroup><creators><creator creatorRole="author" xml:id="cr1" affiliationRef="#a1" corresponding="yes"><personName><givenNames>German</givenNames><familyName>Hoecht</familyName></personName></creator><creator creatorRole="author" xml:id="cr2" affiliationRef="#a2"><personName><givenNames>Patrice</givenNames><familyName>Ricarte</familyName></personName></creator><creator creatorRole="author" xml:id="cr3" affiliationRef="#a3" noteRef="#fn1"><personName><givenNames>Steffen</givenNames><familyName>Bergler</familyName></personName></creator><creator creatorRole="author" xml:id="cr4" affiliationRef="#a1"><personName><givenNames>Evgeny</givenNames><familyName>Landa</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="a1" countryCode="FR"><unparsedAffiliation>OPERA, Bâtiment IFR, rue Jules Ferry, 64000 Pau, France</unparsedAffiliation></affiliation><affiliation xml:id="a2" countryCode="FR"><unparsedAffiliation>Institut Français du Pétrole (IFP), 1‐4 avenue de Bois‐Preau, 92852 Ruiel Malmaison Cedex, France</unparsedAffiliation></affiliation><affiliation xml:id="a3" countryCode="NL"><unparsedAffiliation>Shell International Exploration & Production, Kessler Park 1, 2288 GS Rijswijk, The Netherlands</unparsedAffiliation></affiliation></affiliationGroup><abstractGroup><abstract type="main" xml:lang="en"><title type="main">ABSTRACT</title><p>In common‐reflection‐surface imaging the reflection arrival time field is parameterized by operators that are of higher dimension or order than in conventional methods. Using the common‐reflection‐surface approach locally in the unmigrated prestack data domain opens a potential for trace regularization and interpolation. In most data interpolation methods based on local coherency estimation, a single operator is designed for a target sample and the output amplitude is defined as a weighted average along the operator. This approach may fail in presence of interfering events or strong amplitude and phase variations. In this paper we introduce an alternative scheme in which there is no need for an operator to be defined at the target sample itself. Instead, the amplitude at a target sample is constructed from multiple operators estimated at different positions. In this case one operator may contribute to the construction of several target samples. Vice versa, a target sample might receive contributions from different operators. Operators are determined on a grid which can be sparser than the output grid. This allows to dramatically decrease the computational costs. In addition, the use of multiple operators for a single target sample stabilizes the interpolation results and implicitly allows several contributions in case of interfering events. Due to the considerable computational expense, common‐reflection‐surface interpolation is limited to work in subsets of the prestack data. We present the general workflow of a common‐reflection‐surface‐based regularization/interpolation for 3D data volumes. This workflow has been applied to an OBC common‐receiver volume and binned common‐offset subsets of a 3D marine data set. The impact of a common‐reflection‐surface regularization is demonstrated by means of a subsequent time migration. In comparison to the time migrations of the original and DMO‐interpolated data, the results show particular improvements in view of the continuity of reflections events. This gain is confirmed by an automatic picking of a horizon in the stacked time migrations.</p></abstract></abstractGroup></contentMeta><noteGroup><note xml:id="fn1"><label><sup>‡</sup></label><p>Formerly Geophysical Institute Karlsruhe, Germany</p></note></noteGroup></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Operator‐oriented CRS interpolation</title></titleInfo><titleInfo type="abbreviated"><title>Common‐reflection‐surface‐based interpolation</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Operator‐oriented CRS interpolation</title></titleInfo><name type="personal"><namePart type="given">German</namePart><namePart type="family">Hoecht</namePart><affiliation>OPERA, Bâtiment IFR, rue Jules Ferry, 64000 Pau, France</affiliation><description>Correspondence: *E‐mail: </description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Patrice</namePart><namePart type="family">Ricarte</namePart><affiliation>Institut Français du Pétrole (IFP), 1‐4 avenue de Bois‐Preau, 92852 Ruiel Malmaison Cedex, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Steffen</namePart><namePart type="family">Bergler</namePart><affiliation>Shell International Exploration & Production, Kessler Park 1, 2288 GS Rijswijk, The Netherlands</affiliation><description>Formerly Geophysical Institute Karlsruhe, Germany</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Evgeny</namePart><namePart type="family">Landa</namePart><affiliation>OPERA, Bâtiment IFR, rue Jules Ferry, 64000 Pau, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><place><placeTerm type="text">Oxford, UK</placeTerm></place><dateIssued encoding="w3cdtf">2009-11</dateIssued><edition>Received June 2008, revision accepted November 2008</edition><copyrightDate encoding="w3cdtf">2009</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">25</extent><extent unit="formulas">33</extent><extent unit="references">14</extent></physicalDescription><abstract lang="en">In common‐reflection‐surface imaging the reflection arrival time field is parameterized by operators that are of higher dimension or order than in conventional methods. Using the common‐reflection‐surface approach locally in the unmigrated prestack data domain opens a potential for trace regularization and interpolation. In most data interpolation methods based on local coherency estimation, a single operator is designed for a target sample and the output amplitude is defined as a weighted average along the operator. This approach may fail in presence of interfering events or strong amplitude and phase variations. In this paper we introduce an alternative scheme in which there is no need for an operator to be defined at the target sample itself. Instead, the amplitude at a target sample is constructed from multiple operators estimated at different positions. In this case one operator may contribute to the construction of several target samples. Vice versa, a target sample might receive contributions from different operators. Operators are determined on a grid which can be sparser than the output grid. This allows to dramatically decrease the computational costs. In addition, the use of multiple operators for a single target sample stabilizes the interpolation results and implicitly allows several contributions in case of interfering events. Due to the considerable computational expense, common‐reflection‐surface interpolation is limited to work in subsets of the prestack data. We present the general workflow of a common‐reflection‐surface‐based regularization/interpolation for 3D data volumes. This workflow has been applied to an OBC common‐receiver volume and binned common‐offset subsets of a 3D marine data set. The impact of a common‐reflection‐surface regularization is demonstrated by means of a subsequent time migration. In comparison to the time migrations of the original and DMO‐interpolated data, the results show particular improvements in view of the continuity of reflections events. This gain is confirmed by an automatic picking of a horizon in the stacked time migrations.</abstract><relatedItem type="host"><titleInfo><title>Geophysical Prospecting</title></titleInfo><genre type="Journal">journal</genre><identifier type="ISSN">0016-8025</identifier><identifier type="eISSN">1365-2478</identifier><identifier type="DOI">10.1111/(ISSN)1365-2478</identifier><identifier type="PublisherID">GPR</identifier><part><date>2009</date><detail type="volume"><caption>vol.</caption><number>57</number></detail><detail type="issue"><caption>no.</caption><number>6</number></detail><extent unit="pages"><start>957</start><end>979</end><total>23</total></extent></part></relatedItem><identifier type="istex">0179D900EEEF9035D8481E48D84BD170DC18F484</identifier><identifier type="DOI">10.1111/j.1365-2478.2009.00789.x</identifier><identifier type="ArticleID">GPR789</identifier><accessCondition type="use and reproduction" contentType="copyright">© 2009 European Association of Geoscientists & Engineers</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Blackwell Publishing Ltd</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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|étape= Corpus
|type= RBID
|clé= ISTEX:0179D900EEEF9035D8481E48D84BD170DC18F484
|texte= Operator‐oriented CRS interpolation
}}
| This area was generated with Dilib version V0.6.21. |  |