Noise Reduction with Reflection Supervirtual Interferometry

Kai Lu, Zhaolun Liu, Sherif Hanafy, Gerard T. Schuster

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with far-offset data is that the signal-to-noise ratio (SNR) significantly diminishes with greater offset. In many cases, the poor SNR makes the far-offset reflections imperceptible on the shot records. To mitigate this problem we develop supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their signal-to-noise ratio (SNR). The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger SNRs. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their SNR. Reflection SVI is also applied to a field dataset where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the NMO velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this dataset.
Original languageEnglish (US)
Pages (from-to)1-37
Number of pages37
StatePublished - Jan 9 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): CRG3
Acknowledgements: We thank the sponsors for supporting the Consortium of Subsurface Imaging and Fluid Modeling (CSIM). We also thank KAUST for providing funding by the CRG grant OCRF2014-CRG3-2300. For computer time, this research used the Computing Group and the Supercomputing Laboratory at KAUST.


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