CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12645 > Article
Paper
23 May 2023 Self-supervised deep learning for multi-profile seismic data denoising
Chuanchao Xiong, Chengyun Song, Yin Zhang, Lin Li
Author Affiliations +
Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264555 (2023) https://doi.org/10.1117/12.2681147
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China
Abstract
Self-supervised deep learning has been widely exploited in seismic data denoising. However, most of the current methods take a single seismic profile to reconstruct two new similar profiles by sampling. Then, the two new similar profiles are conducted as input and label respectively to train the model. In fact, there obviously exists similarity and more structural information among adjacent profiles. Therefore, we want to use adjacent multiple profiles to denoise seismic data. But this ideal requires that the neural network model can process multiple profiles. In order to solve this problem, we introduce a supervised model which can input five continuous profiles and denoise the middle profile. In addition, a self-supervised training strategy is proposed for the supervised model to train with no clean profiles. The experimental results on synthetic noise show that our method can achieve a higher Signal-to-Noise Ratio (SNR). According to the experiment on real noise, the proposed algorithm also obtains cleaner and smoother denoising profiles.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Chuanchao Xiong, Chengyun Song, Yin Zhang, and Lin Li "Self-supervised deep learning for multi-profile seismic data denoising", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264555 (23 May 2023); https://doi.org/10.1117/12.2681147
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Denoising
Signal to noise ratio
Deep learning
Neural networks
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top