Reducing data storage in reverse time migration

Weijia Sun; Li-Yun Fu; Zhenxing Yao

doi:10.1071/ASEG2015ab268

24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery

ISSN: 2202-0586
E-ISSN:

oa Reducing data storage in reverse time migration
Authors Weijia Sun^a, Li-Yun Fu^b and Zhenxing Yao^c
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^a Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, 19 Beitucheng Xi Rd 100029, Beijing, , China, [email protected] ^b Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, 19 Beitucheng Xi Rd 100029, Beijing, , China, [email protected] ^c Key Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, 19 Beitucheng Xi Rd 100029, Beijing, , China, [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2015, Issue 24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery, Dec 2015, p. 1 - 4
DOI: https://doi.org/10.1071/ASEG2015ab268
- Published online: 01 Dec 2015

Abstract

Prestack reverse time migration (RTM) requires extensive data storage since it computes wavefields in forward time and accesses wavefields in reverse order. We first review several successful schemes that have been proposed to reduce data storage, but require more computational redundancies. We propose two effective strategies to reduce data storage during RTM. The first strategy is based on the Nyquist sampling theorem, which involves no extra computational cost. The fact is that the time sampling intervals required by numerical algorithms or given by field records is generally several times smaller than that satisfied by the Nyquist sampling theorem. Therefore, we can correlate the source wavefields with the receiver wavefields at the Nyquist time step, which helps decrease storage of time history. The second strategy is based on a lossless compression algorithm, which is widely used in computer science and information theory. The compression approach reduces storage significantly at a little computational cost. Numerical examples show that the two proposed strategies are effective and efficient.

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2026-01-13

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References

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Article Type: Research Article

Keyword(s): compression algorithm; data storage; finite-difference; Nyquist theorem; reverse time migration

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