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Convolutional autoencoder neural network for seismic noise reduction
DARAPUREDDY HARITHA,
NITTALA SATYAVANI*
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CSIR - National Geophysical Research Institute, Hyderabad, India.,
JSE 2022, 31(3), 267–278;
Submitted: 28 June 2021 | Accepted: 14 February 2022 | Published: 1 June 2022
© 2022 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/ )
Abstract
Haritha, D. and Satyavani, N., 2022. Convolutional autoencoder neural network for seismic noise reduction. Journal of Seismic Exploration, 31: 267-278. Seismic noise reduction is one of the main steps in the data processing sequence that aids in proper seismic imaging and interpretation. For the purpose of noise reduction and to recover weaker / masked signals, we propose the scheme of an unsupervised convolutional autoencoder neural network. Cross-entropy is used as the loss function in the network. The adaptive moment estimation plays the role of a backpropagation algorithm that can optimize the loss function. The key parameters of the network, like convolutional layers, filter size, and learning rate have been selected after performing a series of tests with different values for each of the parameters and those results are also presented here. We show that the present network applied to the seismic data shows improvement in the reflections and also allows us to recover some of the weaker / masked signals. The results show that the proposed method is successful in suppressing the noise and enhancing the seismic signals.
Keywords
seismic data
noise reduction
convolutional layers
filter size
convolutional autoencoder
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