Convolutional Sparse Coding for Noise Attenuation of Seismic Data

Zhaolun Liu, Kai Lu, Xiaodan Ge

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Convolutional sparse coding (CSC) is proposed to attenuate noise for seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training phase and the denoising phase. The seismic data with a relatively high signal-to-noise ratio are chosen for training to get the learned basis functions. Then we use all (or a subset) of the basis functions to attenuate the random or coherent noise in the seismic data. Numerical experiments on synthetic and field data indicate that the proposed method achieves good performance for denoising random and coherent noise and separating the ground roll.
Original languageEnglish (US)
Title of host publicationSEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17-19 September 2018
PublisherSociety of Exploration Geophysicists and the Chinese Geophysical Society
StatePublished - Dec 14 2018

Bibliographical note

KAUST Repository Item: Exported on 2021-03-09
Acknowledgements: The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. We are grateful to the sponsors of the Center for Subsurface Imaging and Modeling Consortium for their financial support. For computer time, this research used the resources of the Supercomputing Laboratory at KAUST and the IT Research Computing Group. We thank them forproviding the computational resources required for carryingout this work.


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