Separation of different order multiples based on focal transform and SRME
Zhina Li 1 2 5 Zhenchun Li 1 2 Peng Wang 3 Yueming Ye 41 China University of Petroleum (East China), School of Geosciences, Qingdao 266580, China.
2 Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China.
3 China University of Petroleum (East China), Research Institute of Unconventional Oil and Gas and Renewable Energy, Qingdao 266580, China.
4 PetroChina Hangzhou Research Institute of Geology, Hangzhou 310000, China.
5 Corresponding author. Email: lizhina00@163.com
Exploration Geophysics 49(4) 519-529 https://doi.org/10.1071/EG16084
Submitted: 11 July 2016 Accepted: 7 July 2017 Published: 14 September 2017
Abstract
Multiples that are reflected more than once beneath the surface are also real reflections from subsurface interfaces. They are typically characterised by smaller reflection angles, longer travel paths and wider illumination ranges than primaries. With the increase in reflected times at the surface, the orders of multiples included in the seismic data will increase. Multiples with higher orders will cover a larger area and contain more structural information. Therefore, if these high order multiples are utilised properly, subsurface structures will be better described. Since the wavefields of different order multiples are extraordinarily complex and research regarding the separation of different order multiples is sparse, conventional techniques for the processing of multiples cannot effectively deal with different order multiples. Considering that the focal transform can flexibly perform order reduction or elevation of multiples and the surface-related multiple elimination (SRME) can effectively separate primaries and multiples, a practical and novel method based on both the focal transform and SRME is proposed to separate different order multiples in this paper. Based on the theoretical derivation, a detailed workflow of the proposed method is provided. Taking full advantages of the focal transform (weighted cross-correlation) and SRME (weighted convolution), the proposed method can effectively separate different order multiples. Numerical examples further illustrate the feasibility and validity of the promising method.
Key words: different order multiples, focal transform, least-squares subtraction, SRME.
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