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Efficient deplending using median filtering without correct normal moveout – with comparison on migrated images

MIN BAI,  JUAN WU
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School of Resources and Environment, North China University of Water Resources and Electric Power, Zhengzhou 450045, P.R. China. baimin@ncwu.edu.cn,
JSE 2017, 26(5), 455–478;
Submitted: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

The benefits of simultaneous source acquisition are compromised by the challenges of dealing with intense blending noise. While the median filtering approach can be effectively used in attenuating blending interference due to its superb performance in rejecting spiky noise, it requires two or three times of normal moveout (NMO) based velocity analysis in order to exactly flatten the seismic data and thus it is computationally expensive. In this paper, we propose an efficient deblending framework that is based on a modified median filtering approach and does not require a correct NMO correction. The modified median filtering approach depends on a novel median filter that can spatially change the filter length and can deal with curved events due to the incorrect NMO. The median filter with variable window length is an adaptive median filter, thus it can be conveniently used in the presented processing workflow without the need of much human input. We not only compare the deblending performance in the data space, but also present detailed comparison in-the image space. An important criterion we use to compare the deblending performance is the local correlation between deblended data and removed blending noise.

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