Tomographic full waveform inversion using normalized integral wavefield

Yunseok Choi, Mahesh Kalita

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Thanks to smooth intrinsic nature of normalized integral wavefield (NIW), full waveform inversion (FWI) using NIW has a potential to retrieve the long wavelength model parameters from seismic data even in the absence of low frequencies. However, if reflection events are dominant in the data, this strategy might fail in rendering a convergent result. To steer the focus of inversion more onto the matching of first arrivals' kinematics, hence the tomographic result, we construct objective functions using the logarithmic NIW and the NIW raised to the power of small positive number for FWI. We derive the gradient formulae of the new objective functions using the adjoint-state method, thus the computational cost of our approaches remains compatible to that of the conventional FWI. Synthetic data example on 2D SEG/EAGE salt-dome model, in which reflections are prevalent, indicates that our FWI approaches generate tomographic convergent results even without low frequency information.
Original languageEnglish (US)
Pages (from-to)29-36
Number of pages8
JournalJournal of Applied Geophysics
Volume165
DOIs
StatePublished - Apr 8 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research was supported by the Basic Research Project (GP2017-015) of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Ministry of Science, ICT and Future Planning of Korea. This work was also supported by the Energy Efficiency & Resources Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20182510102470). We are grateful to King Abdullah University of Science and Technology for financial supports. For computer time, this research used the resources of the Super Computing Laboratory at KAUST in Thuwal, Saudi Arabia. We thank Tariq Alkhalifah for supporting this research.

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