Random Noise Attenuation of Seismic Data via Self-Supervised Bayesian Deep Learning
Author:
Affiliation:
1. School of Sciences, Xi’an Technological University, Xi’an, China
2. School of Freshmen, Xi’an Technological University, Xi’an, China
3. School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, China
Funder
National Natural Science Foundation of China
Key Research and Development Program of Shaanxi Province
Science and Technology Planning Project of Shaanxi Province
Postgraduate Education and Teaching Reform Project of Xi’an Technological University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10185996.pdf?arnumber=10185996
Reference70 articles.
1. Attenuation of noise and simultaneous source interference using wavelet denoising
2. A hybrid ring artifact reduction algorithm based on CNN in CT images
3. The Use of Wavelet-Based Denoising Techniques to Enhance the First-Arrival Picking on Seismic Traces
4. NS2NS: Self-Learning for Seismic Image Denoising
5. Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform
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2. PD-VBS: Real Seismic Image Denoising With Pixel-Shuffle Down-Sampling and Visible Blind-Spots;IEEE Transactions on Geoscience and Remote Sensing;2024
3. Unsupervised 3-D Seismic Erratic Noise Attenuation With Robust Tensor Deep Learning;IEEE Transactions on Geoscience and Remote Sensing;2024
4. A novel approach for seismic signal denoising using optimized discrete wavelet transform via honey badger optimization algorithm;Journal of Applied Geophysics;2023-12
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