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.
|Title of host publication||81st EAGE Conference and Exhibition 2019|
|Number of pages||5|
|Publication status||Published - 2019|
|Event||81st EAGE Conference and Exhibition 2019 - ExCeL Centre, London, United Kingdom|
Duration: 3 Jun 2019 → 6 Jun 2019
|Conference||81st EAGE Conference and Exhibition 2019|
|Period||3/06/19 → 6/06/19|