Reinforcement Learning-Based Denoising Model for Seismic Random Noise Attenuation
Author:
Affiliation:
1. Department of Information, College of Communication and Engineering, Jilin University, Changchun, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10106047.pdf?arnumber=10106047
Reference67 articles.
1. Matching-pursuit-based spatial-trace time-frequency peak filtering for seismic random noise attenuation;lin;IEEE Geosci Remote Sens Lett,2014
2. Random noise attenuation using local signal-and-noise orthogonalization
3. Retracted: Application of variational mode decomposition to seismic random noise reduction
4. A Diffusion Filter Based Scheme to Denoise Seismic Attributes and Improve Predicted Porosity Volume
5. Incoherent Noise Suppression of Seismic Data Based on Robust Low-Rank Approximation
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3. Self-Supervised Seismic Random Noise Suppression With Higher-Quality Training Data Based on Similarity Differences;IEEE Access;2024
4. Unsupervised 3-D Seismic Erratic Noise Attenuation With Robust Tensor Deep Learning;IEEE Transactions on Geoscience and Remote Sensing;2024
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