A fully unsupervised and highly generalized deep learning approach for random noise suppression-Reference-Cited by-同舟云学术

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A fully unsupervised and highly generalized deep learning approach for random noise suppression

Published:2021-02-26 Issue:4 Volume:69 Page:709-726
ISSN:0016-8025
Container-title:Geophysical Prospecting
language:en
Short-container-title:Geophysical Prospecting

Author:

Saad Omar M.¹²^ORCID,Chen Yangkang¹^ORCID

Affiliation:

1. Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences Zhejiang University Hangzhou 102249 China

2. ENSN Lab, Seismology Department National Research Institute of Astronomy and Geophysics (NRIAG) Helwan 11731 Egypt

Funder

Zhejiang University

Publisher

Wiley

Subject

Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2478.13062

Reference48 articles.

1. Lateral prediction for noise attenuation by t-x and f-x techniques

2. Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation

3. Random noise reduction;Canales L;54th SEG Annual International meeting, Expanded Abstracts,1984

4. New insights into the mechanisms of seismicity in the Azle area, North Texas

5. Random noise attenuation using local signal-and-noise orthogonalization

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