Target-oriented inversion with least-squares waveform redatuming

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Thanks to the rapid growth in high-performance computing technology, full waveform inversion (FWI) has been successfully implemented in many field data applications. Nevertheless, it is still extremely expensive to perform a multi-parameter FWI over the whole subsurface model space that often needs to be discretized consistently using a fine grid, to delineate for example reservoir scale features. Building on the recent development of target-oriented imaging and inversion, we split the subsurface space into the overburden, above a datum level, and the target zone beneath the datum. Our objective is to retrieve the virtual data at a target level and then estimate a high-resolution model of the critical, possibly reservoir, zone. We first build an overburden velocity model using FWI with the data containing frequencies up to 20 Hz and then retrieve a virtual dataset at the datum survey from the data recorded at the Earth's surface. A least-squares optimization of the waveform redatuming is used for the virtual data retrieval. We finally invert for the target zone using the estimated highly reduced in size, but containing high-frequency, dataset. It will lead to an obvious boost in the convergence rate and bring down the memory and computational cost, even though a finer grid is used for the redatuming and the following inversion of the target zone. The Chevron 2014 blind test dataset is used to demonstrate the effectiveness of this strategy.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2019
PublisherSociety of Exploration Geophysicists
Number of pages5
StatePublished - Aug 10 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors thank KAUST for its support and the SWAG group for the collaborative environment. For computer time, this research used the resources of the KAUST Supercomputing Laboratory (KSL).


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