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Reverse time migration by Krylov subspace reduced order modeling.

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Title: Reverse time migration by Krylov subspace reduced order modeling.
Authors: Basir, Hadi Mahdavi1, Javaherian, Abdolrahim1,2 javaheri@ut.ac.ir, Shomali, Zaher Hossein2,3, Firouz-Abadi, Roohollah Dehghani4, Gholamy, Shaban Ali5
Source: Journal of Applied Geophysics. Apr2018, Vol. 151, p298-308. 11p.
Subjects: Imaging systems in seismology, Krylov subspace, Computer simulation, Parallel computers, Algorithms, Depth migration (Geophysics)
Abstract: Imaging is a key step in seismic data processing. To date, a myriad of advanced pre-stack depth migration approaches have been developed; however, reverse time migration (RTM) is still considered as the high-end imaging algorithm. The main limitations associated with the performance cost of reverse time migration are the intensive computation of the forward and backward simulations, time consumption, and memory allocation related to imaging condition. Based on the reduced order modeling, we proposed an algorithm, which can be adapted to all the aforementioned factors. Our proposed method benefit from Krylov subspaces method to compute certain mode shapes of the velocity model computed by as an orthogonal base of reduced order modeling. Reverse time migration by reduced order modeling is helpful concerning the highly parallel computation and strongly reduces the memory requirement of reverse time migration. The synthetic model results showed that suggested method can decrease the computational costs of reverse time migration by several orders of magnitudes, compared with reverse time migration by finite element method. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Imaging is a key step in seismic data processing. To date, a myriad of advanced pre-stack depth migration approaches have been developed; however, reverse time migration (RTM) is still considered as the high-end imaging algorithm. The main limitations associated with the performance cost of reverse time migration are the intensive computation of the forward and backward simulations, time consumption, and memory allocation related to imaging condition. Based on the reduced order modeling, we proposed an algorithm, which can be adapted to all the aforementioned factors. Our proposed method benefit from Krylov subspaces method to compute certain mode shapes of the velocity model computed by as an orthogonal base of reduced order modeling. Reverse time migration by reduced order modeling is helpful concerning the highly parallel computation and strongly reduces the memory requirement of reverse time migration. The synthetic model results showed that suggested method can decrease the computational costs of reverse time migration by several orders of magnitudes, compared with reverse time migration by finite element method. [ABSTRACT FROM AUTHOR]
ISSN:09269851
DOI:10.1016/j.jappgeo.2018.02.010