A multi‐data training method for a deep neural network to improve the separation effect of simultaneous‐source data-Reference-Cited by-同舟云学术

A multi‐data training method for a deep neural network to improve the separation effect of simultaneous‐source data

Published:2022-11-14 Issue:1 Volume:71 Page:63-84
ISSN:0016-8025
Container-title:Geophysical Prospecting
language:en
Short-container-title:Geophysical Prospecting

Author:

Wang Kunxi¹²^ORCID,Mao Weijian¹,Song Huan¹^ORCID,Evinemi E. Isaac¹²

Affiliation:

1. State Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan China

2. University of Chinese Academy of Sciences Beijing China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2478.13276

Reference75 articles.

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2. Akerberg P. Hampson G. Rickett J. Martin H.&Cole J.(2008)Simultaneous‐source separation by sparse radon transform. SEG Technical Program Expanded Abstracts pp.2801–2805.

3. Baardman R.&Tsingas C.(2019)Classification and suppression of blending noise using convolutional neural networks.SPE Middle East Oil and Gas Show and Conference.

4. A new look at marine simultaneous sources

5. Changing the mindset in seismic data acquisition

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