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Stacking seismic data based on principal component analysis

JUAN WU,  MIN BAI
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School of Resources and Environment, North China University of Water Resources and Electric Power, Zhengzhou 450045, P.R. China,
JSE 2018, 27(4), 331–348;
Submitted: 28 August 2017 | Accepted: 20 June 2018 | Published: 1 August 2018
© 2018 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

Wu, J. and Bai, M., 2018. Stacking seismic data based on principal component analysis. Journal of Seismic Exploration, 27: 331-348. Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional equal-weight seismic stacking method cannot obtain optimal performance when the ambient noise is extremely strong. We propose applying a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. We use both synthetic and field data examples to demonstrate the performance of the presented method.

Keywords
seismic imaging
stacking
principal component analysis
low rank approximation
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