Unsupervised Seismic Random Noise Attenuation Based on Deep Convolutional Neural Network

Unsupervised Seismic Random Noise Attenuation Based on Deep Convolutional Neural Network

Random noise attenuation is one of the most essential steps in seismic signal processing. We propose a novel approach to attenuate seismic random noise based on deep convolutional neural network (CNN) in an unsupervised learning manner. First, normalization and patch sampling are required to build t...

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Bibliographic Details
Main Authors: Mi Zhang, Yang Liu, Yangkang Chen
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Seismic data
noise attenuation
deep convolutional neural network
unsupervised learning
Online Access:https://ieeexplore.ieee.org/document/8932397/

Internet

https://ieeexplore.ieee.org/document/8932397/

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