Abstract
Surface-related multiple elimination (SRME) is a solid and effective approach for primary estimation. However, due to the imperfections in data and method (e.g. coarsely-sampled dataset and balancing effect of adaptive subtraction) multiple energy leakage is commonly seen in the results of SRME-predicted primaries. Assuming that the primaries and multiples do not correlate locally in the time-space domain, we are able to extract the leaked multiples from the initially estimated primaries using local primary-and-multiple orthogonalization. The proposed framework consists of two steps: an initial primary/multiple estimation step and a multiple-leakage extraction step. The initial step corresponds to SRME, which produces the initial estimated primary and multiple models. The second step is based on local primary-and-multiple orthogonalization to retrieve the leaked multiples, which can be seen as a remedy for correcting the initial estimated primary and multiple models. Thus, we can obtain a better primary output which has much less leaked multiple energy. We demonstrate a good performance of our proposed framework on both synthetic and field data, where it repairs the leakage of standard adaptive subtraction.
Original language | English |
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Title of host publication | 81st EAGE Conference and Exhibition 2019 |
Editors | Howard Leach |
Publisher | EAGE |
Number of pages | 5 |
ISBN (Electronic) | 9789462822894 |
DOIs | |
Publication status | Published - 2019 |
Event | 81st EAGE Conference and Exhibition 2019 - ExCeL Centre, London, United Kingdom Duration: 3 Jun 2019 → 6 Jun 2019 https://eage.eventsair.com/81st-eage-annual-conference-and-exhibtion/ |
Publication series
Name | 81st EAGE Conference and Exhibition 2019 |
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Conference
Conference | 81st EAGE Conference and Exhibition 2019 |
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Country/Territory | United Kingdom |
City | London |
Period | 3/06/19 → 6/06/19 |
Internet address |