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3D Marchenko applications: implementation and examples
ABSTRACT We implement the 3D Marchenko equations to retrieve responses to virtual sources inside the subsurface. For this, we require reflection data at the surface of the Earth that contain no free‐surface multiples and are densely sampled in space. The required 3D reflection data volume is very la...
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Published in: | Geophysical Prospecting 2022-01, Vol.70 (1), p.35-56 |
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creator | Brackenhoff, Joeri Thorbecke, Jan Meles, Giovanni Koehne, Victor Barrera, Diego Wapenaar, Kees |
description | ABSTRACT
We implement the 3D Marchenko equations to retrieve responses to virtual sources inside the subsurface. For this, we require reflection data at the surface of the Earth that contain no free‐surface multiples and are densely sampled in space. The required 3D reflection data volume is very large and solving the Marchenko equations requires a significant amount of computational cost. To limit the cost, we apply floating point compression to the reflection data to reduce their volume and the loading time from disk. We apply the Marchenko implementation to numerical reflection data to retrieve accurate Green's functions inside the medium and use these reflection data to apply imaging. This requires the simulation of many virtual source points, which we circumvent using virtual plane‐wave sources instead of virtual point sources. Through this method, we retrieve the angle‐dependent response of a source from a depth level rather than of a point. We use these responses to obtain angle‐dependent structural images of the subsurface, free of contamination from wrongly imaged internal multiples. These images have less lateral resolution than those obtained using virtual point sources, but are more efficiently retrieved. |
doi_str_mv | 10.1111/1365-2478.13151 |
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We implement the 3D Marchenko equations to retrieve responses to virtual sources inside the subsurface. For this, we require reflection data at the surface of the Earth that contain no free‐surface multiples and are densely sampled in space. The required 3D reflection data volume is very large and solving the Marchenko equations requires a significant amount of computational cost. To limit the cost, we apply floating point compression to the reflection data to reduce their volume and the loading time from disk. We apply the Marchenko implementation to numerical reflection data to retrieve accurate Green's functions inside the medium and use these reflection data to apply imaging. This requires the simulation of many virtual source points, which we circumvent using virtual plane‐wave sources instead of virtual point sources. Through this method, we retrieve the angle‐dependent response of a source from a depth level rather than of a point. We use these responses to obtain angle‐dependent structural images of the subsurface, free of contamination from wrongly imaged internal multiples. These images have less lateral resolution than those obtained using virtual point sources, but are more efficiently retrieved.</description><identifier>ISSN: 0016-8025</identifier><identifier>EISSN: 1365-2478</identifier><identifier>DOI: 10.1111/1365-2478.13151</identifier><language>eng</language><publisher>Houten: Wiley Subscription Services, Inc</publisher><subject>Compression ; Computing costs ; Earth surface ; Floating point arithmetic ; Green's function ; Green's functions ; Mathematical analysis ; Numerical study ; Reflection ; Seismics ; Signal processing</subject><ispartof>Geophysical Prospecting, 2022-01, Vol.70 (1), p.35-56</ispartof><rights>2021 The Authors. published by John Wiley & Sons Ltd on behalf of European Association of Geoscientists & Engineers</rights><rights>2021. This article 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-c3561-49a30b476e331ae7b9fc3d266487cb7656e01714e4a81769d2958d71c68be0b43</citedby><cites>FETCH-LOGICAL-c3561-49a30b476e331ae7b9fc3d266487cb7656e01714e4a81769d2958d71c68be0b43</cites><orcidid>0000-0002-2960-9587 ; 0000-0003-1228-7283 ; 0000-0002-1620-8282 ; 0000-0001-7697-1194 ; 0000-0002-5219-0868 ; 0000-0002-7338-1735</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Brackenhoff, Joeri</creatorcontrib><creatorcontrib>Thorbecke, Jan</creatorcontrib><creatorcontrib>Meles, Giovanni</creatorcontrib><creatorcontrib>Koehne, Victor</creatorcontrib><creatorcontrib>Barrera, Diego</creatorcontrib><creatorcontrib>Wapenaar, Kees</creatorcontrib><title>3D Marchenko applications: implementation and examples</title><title>Geophysical Prospecting</title><description>ABSTRACT
We implement the 3D Marchenko equations to retrieve responses to virtual sources inside the subsurface. For this, we require reflection data at the surface of the Earth that contain no free‐surface multiples and are densely sampled in space. The required 3D reflection data volume is very large and solving the Marchenko equations requires a significant amount of computational cost. To limit the cost, we apply floating point compression to the reflection data to reduce their volume and the loading time from disk. We apply the Marchenko implementation to numerical reflection data to retrieve accurate Green's functions inside the medium and use these reflection data to apply imaging. This requires the simulation of many virtual source points, which we circumvent using virtual plane‐wave sources instead of virtual point sources. Through this method, we retrieve the angle‐dependent response of a source from a depth level rather than of a point. We use these responses to obtain angle‐dependent structural images of the subsurface, free of contamination from wrongly imaged internal multiples. 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We implement the 3D Marchenko equations to retrieve responses to virtual sources inside the subsurface. For this, we require reflection data at the surface of the Earth that contain no free‐surface multiples and are densely sampled in space. The required 3D reflection data volume is very large and solving the Marchenko equations requires a significant amount of computational cost. To limit the cost, we apply floating point compression to the reflection data to reduce their volume and the loading time from disk. We apply the Marchenko implementation to numerical reflection data to retrieve accurate Green's functions inside the medium and use these reflection data to apply imaging. This requires the simulation of many virtual source points, which we circumvent using virtual plane‐wave sources instead of virtual point sources. Through this method, we retrieve the angle‐dependent response of a source from a depth level rather than of a point. We use these responses to obtain angle‐dependent structural images of the subsurface, free of contamination from wrongly imaged internal multiples. These images have less lateral resolution than those obtained using virtual point sources, but are more efficiently retrieved.</abstract><cop>Houten</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1111/1365-2478.13151</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-2960-9587</orcidid><orcidid>https://orcid.org/0000-0003-1228-7283</orcidid><orcidid>https://orcid.org/0000-0002-1620-8282</orcidid><orcidid>https://orcid.org/0000-0001-7697-1194</orcidid><orcidid>https://orcid.org/0000-0002-5219-0868</orcidid><orcidid>https://orcid.org/0000-0002-7338-1735</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Compression Computing costs Earth surface Floating point arithmetic Green's function Green's functions Mathematical analysis Numerical study Reflection Seismics Signal processing |
title | 3D Marchenko applications: implementation and examples |
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