A CNN Based Physical Constraint Modifier Model for Reflectivity Inversion-Reference-Cited by-同舟云学术

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A CNN Based Physical Constraint Modifier Model for Reflectivity Inversion

Published:2023-07-09 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Machine Learning and Cybernetics (ICMLC)
language:
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Author:

Shi Yun-Peng¹,Wang Chang-Zhong²,Pan Shulin³,Song Guojie⁴,Min Fan¹

Affiliation:

1. School of Computer Science, Southwest Petroleum University,Chengdu,China,610500

2. School of Mathematical Sciences, Bohai University,Jinzhou,China,121000

3. School of Earth Science and Technology, Southwest Petroleum University,Chengdu,China,610500

4. School of Sciences, Southwest Petroleum University,Chengdu,China,610500

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10327787/10327919/10327932.pdf?arnumber=10327932

Reference16 articles.

1. Sparse seismic deconvolution via recurrent neural network

2. Deconvolution using Neural Networks

3. High resolution inversion of seismic wavelet and reflectivity using iterative deep neural networks

4. Deep learning for multitrace sparse-spike deconvolution

5. A Sparse Spike Deconvolution Algorithm Based on a Recurrent Neural Network and the Iterative Shrinkage-Thresholding Algorithm

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