Time Reversal Migration for Passive Sources Using a Maximum Variance Imaging Condition

Handle URI:
http://hdl.handle.net/10754/624916
Title:
Time Reversal Migration for Passive Sources Using a Maximum Variance Imaging Condition
Authors:
Wang, H.; Alkhalifah, Tariq Ali ( 0000-0002-9363-9799 )
Abstract:
The conventional time-reversal imaging approach for micro-seismic or passive source location is based on focusing the back-propagated wavefields from each recorded trace in a source image. It suffers from strong background noise and limited acquisition aperture, which may create unexpected artifacts and cause error in the source location. To overcome such a problem, we propose a new imaging condition for microseismic imaging, which is based on comparing the amplitude variance in certain windows, and use it to suppress the artifacts as well as find the right location for passive sources. Instead of simply searching for the maximum energy point in the back-propagated wavefield, we calculate the amplitude variances over a window moving in both space and time axis to create a highly resolved passive event image. The variance operation has negligible cost compared with the forward/backward modeling operations, which reveals that the maximum variance imaging condition is efficient and effective. We test our approach numerically on a simple three-layer model and on a piece of the Marmousi model as well, both of which have shown reasonably good results.
KAUST Department:
King Abdullah University of Science & Technology
Citation:
Wang H, Alkhalifah T (2017) Time Reversal Migration for Passive Sources Using a Maximum Variance Imaging Condition. 79th EAGE Conference and Exhibition 2017. Available: http://dx.doi.org/10.3997/2214-4609.201701262.
Publisher:
EAGE Publications BV
Journal:
79th EAGE Conference and Exhibition 2017
Issue Date:
26-May-2017
DOI:
10.3997/2214-4609.201701262
Type:
Conference Paper
Additional Links:
http://www.earthdoc.org/publication/publicationdetails/?publication=88978
Appears in Collections:
Conference Papers

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, H.en
dc.contributor.authorAlkhalifah, Tariq Alien
dc.date.accessioned2017-06-12T08:33:42Z-
dc.date.available2017-06-12T08:33:42Z-
dc.date.issued2017-05-26en
dc.identifier.citationWang H, Alkhalifah T (2017) Time Reversal Migration for Passive Sources Using a Maximum Variance Imaging Condition. 79th EAGE Conference and Exhibition 2017. Available: http://dx.doi.org/10.3997/2214-4609.201701262.en
dc.identifier.doi10.3997/2214-4609.201701262en
dc.identifier.urihttp://hdl.handle.net/10754/624916-
dc.description.abstractThe conventional time-reversal imaging approach for micro-seismic or passive source location is based on focusing the back-propagated wavefields from each recorded trace in a source image. It suffers from strong background noise and limited acquisition aperture, which may create unexpected artifacts and cause error in the source location. To overcome such a problem, we propose a new imaging condition for microseismic imaging, which is based on comparing the amplitude variance in certain windows, and use it to suppress the artifacts as well as find the right location for passive sources. Instead of simply searching for the maximum energy point in the back-propagated wavefield, we calculate the amplitude variances over a window moving in both space and time axis to create a highly resolved passive event image. The variance operation has negligible cost compared with the forward/backward modeling operations, which reveals that the maximum variance imaging condition is efficient and effective. We test our approach numerically on a simple three-layer model and on a piece of the Marmousi model as well, both of which have shown reasonably good results.en
dc.publisherEAGE Publications BVen
dc.relation.urlhttp://www.earthdoc.org/publication/publicationdetails/?publication=88978en
dc.rightsGold Open Accessen
dc.rightsArchived with thanks to 79th EAGE Conference and Exhibition 2017en
dc.titleTime Reversal Migration for Passive Sources Using a Maximum Variance Imaging Conditionen
dc.typeConference Paperen
dc.contributor.departmentKing Abdullah University of Science & Technologyen
dc.identifier.journal79th EAGE Conference and Exhibition 2017en
dc.eprint.versionPublisher's Version/PDFen
kaust.authorWang, H.en
kaust.authorAlkhalifah, Tariq Alien
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