Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising

Yuan Xu; Qing Wang; Shiguang Guo

doi:doi:10.11648/j.ijssn.20221002.13

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Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising

Yuan Xu, Qing Wang, Shiguang Guo

Published in International Journal of Sensors and Sensor Networks (Volume 10, Issue 2)

Received: 13 September 2022 Accepted: 29 September 2022 Published: 17 October 2022

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Abstract

Efficient and high-quality processing of seismic data collected by geophone sensors is the core of successful seismic exploration. Seismic denoising is a key step in seismic data processing. Traditional seismic denoising relies on manual empirical parameter selection and comparative analysis, which is time-consuming and limited by subjective errors. Deep learning methods based on convolutional neural networks (CNN) have improved the efficiency of denoising massive amounts of seismic data and reduced the manual errors of traditional methods. However, most CNN methods only consider data loss and are weak in recovering the structure of seismic signals, resulting in severe attenuation of some seismic traces in the recovered effective signals, reducing the continuity of seismic events and the quality of seismic data. Generative adversarial networks (GAN), a popular method for deep learning with unique adversarial ideas and powerful feature extraction capabilities, can overcome the limitations of CNN methods in the field of seismic data denoising. This paper firstly introduces the classification and development process of seismic denoising. Then, starting from the principle of GAN, it introduces the workflow of the original GAN, the objective function in the training process, the existing problems of the original GAN and some mainstream solutions to these problems, and introduces the commonly used model of GAN in the field of earthquake denoising. besides, it summarizes and analyzes the current application and improvement innovation of GAN in the field of seismic denoising, and analyzes the application of GAN in the field of seismic denoising from two aspects of supervised learning and unsupervised learning with examples. Finally, the prospect of GAN for seismic denoising in the future is prospected.

Published in	International Journal of Sensors and Sensor Networks (Volume 10, Issue 2)
DOI	10.11648/j.ijssn.20221002.13
Page(s)	33-50
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Deep Learning, GAN, Seismic Denoising

References

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APA Style

Yuan Xu, Qing Wang, Shiguang Guo. (2022). Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising. International Journal of Sensors and Sensor Networks, 10(2), 33-50. https://doi.org/10.11648/j.ijssn.20221002.13

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ACS Style

Yuan Xu; Qing Wang; Shiguang Guo. Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising. Int. J. Sens. Sens. Netw. 2022, 10(2), 33-50. doi: 10.11648/j.ijssn.20221002.13

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Yuan Xu, Qing Wang, Shiguang Guo. Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising. Int J Sens Sens Netw. 2022;10(2):33-50. doi: 10.11648/j.ijssn.20221002.13

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@article{10.11648/j.ijssn.20221002.13,
  author = {Yuan Xu and Qing Wang and Shiguang Guo},
  title = {Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {10},
  number = {2},
  pages = {33-50},
  doi = {10.11648/j.ijssn.20221002.13},
  url = {https://doi.org/10.11648/j.ijssn.20221002.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20221002.13},
  abstract = {Efficient and high-quality processing of seismic data collected by geophone sensors is the core of successful seismic exploration. Seismic denoising is a key step in seismic data processing. Traditional seismic denoising relies on manual empirical parameter selection and comparative analysis, which is time-consuming and limited by subjective errors. Deep learning methods based on convolutional neural networks (CNN) have improved the efficiency of denoising massive amounts of seismic data and reduced the manual errors of traditional methods. However, most CNN methods only consider data loss and are weak in recovering the structure of seismic signals, resulting in severe attenuation of some seismic traces in the recovered effective signals, reducing the continuity of seismic events and the quality of seismic data. Generative adversarial networks (GAN), a popular method for deep learning with unique adversarial ideas and powerful feature extraction capabilities, can overcome the limitations of CNN methods in the field of seismic data denoising. This paper firstly introduces the classification and development process of seismic denoising. Then, starting from the principle of GAN, it introduces the workflow of the original GAN, the objective function in the training process, the existing problems of the original GAN and some mainstream solutions to these problems, and introduces the commonly used model of GAN in the field of earthquake denoising. besides, it summarizes and analyzes the current application and improvement innovation of GAN in the field of seismic denoising, and analyzes the application of GAN in the field of seismic denoising from two aspects of supervised learning and unsupervised learning with examples. Finally, the prospect of GAN for seismic denoising in the future is prospected.},
 year = {2022}
}

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TY  - JOUR
T1  - Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising
AU  - Yuan Xu
AU  - Qing Wang
AU  - Shiguang Guo
Y1  - 2022/10/17
PY  - 2022
N1  - https://doi.org/10.11648/j.ijssn.20221002.13
DO  - 10.11648/j.ijssn.20221002.13
T2  - International Journal of Sensors and Sensor Networks
JF  - International Journal of Sensors and Sensor Networks
JO  - International Journal of Sensors and Sensor Networks
SP  - 33
EP  - 50
PB  - Science Publishing Group
SN  - 2329-1788
UR  - https://doi.org/10.11648/j.ijssn.20221002.13
AB  - Efficient and high-quality processing of seismic data collected by geophone sensors is the core of successful seismic exploration. Seismic denoising is a key step in seismic data processing. Traditional seismic denoising relies on manual empirical parameter selection and comparative analysis, which is time-consuming and limited by subjective errors. Deep learning methods based on convolutional neural networks (CNN) have improved the efficiency of denoising massive amounts of seismic data and reduced the manual errors of traditional methods. However, most CNN methods only consider data loss and are weak in recovering the structure of seismic signals, resulting in severe attenuation of some seismic traces in the recovered effective signals, reducing the continuity of seismic events and the quality of seismic data. Generative adversarial networks (GAN), a popular method for deep learning with unique adversarial ideas and powerful feature extraction capabilities, can overcome the limitations of CNN methods in the field of seismic data denoising. This paper firstly introduces the classification and development process of seismic denoising. Then, starting from the principle of GAN, it introduces the workflow of the original GAN, the objective function in the training process, the existing problems of the original GAN and some mainstream solutions to these problems, and introduces the commonly used model of GAN in the field of earthquake denoising. besides, it summarizes and analyzes the current application and improvement innovation of GAN in the field of seismic denoising, and analyzes the application of GAN in the field of seismic denoising from two aspects of supervised learning and unsupervised learning with examples. Finally, the prospect of GAN for seismic denoising in the future is prospected.
VL  - 10
IS  - 2
ER  -

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Author Information

Yuan Xu

Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing, China
Qing Wang

Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing, China
Shiguang Guo

Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing, China

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APA Style

Yuan Xu, Qing Wang, Shiguang Guo. (2022). Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising. International Journal of Sensors and Sensor Networks, 10(2), 33-50. https://doi.org/10.11648/j.ijssn.20221002.13

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ACS Style

Yuan Xu; Qing Wang; Shiguang Guo. Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising. Int. J. Sens. Sens. Netw. 2022, 10(2), 33-50. doi: 10.11648/j.ijssn.20221002.13

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AMA Style

Yuan Xu, Qing Wang, Shiguang Guo. Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising. Int J Sens Sens Netw. 2022;10(2):33-50. doi: 10.11648/j.ijssn.20221002.13

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@article{10.11648/j.ijssn.20221002.13,
  author = {Yuan Xu and Qing Wang and Shiguang Guo},
  title = {Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {10},
  number = {2},
  pages = {33-50},
  doi = {10.11648/j.ijssn.20221002.13},
  url = {https://doi.org/10.11648/j.ijssn.20221002.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20221002.13},
  abstract = {Efficient and high-quality processing of seismic data collected by geophone sensors is the core of successful seismic exploration. Seismic denoising is a key step in seismic data processing. Traditional seismic denoising relies on manual empirical parameter selection and comparative analysis, which is time-consuming and limited by subjective errors. Deep learning methods based on convolutional neural networks (CNN) have improved the efficiency of denoising massive amounts of seismic data and reduced the manual errors of traditional methods. However, most CNN methods only consider data loss and are weak in recovering the structure of seismic signals, resulting in severe attenuation of some seismic traces in the recovered effective signals, reducing the continuity of seismic events and the quality of seismic data. Generative adversarial networks (GAN), a popular method for deep learning with unique adversarial ideas and powerful feature extraction capabilities, can overcome the limitations of CNN methods in the field of seismic data denoising. This paper firstly introduces the classification and development process of seismic denoising. Then, starting from the principle of GAN, it introduces the workflow of the original GAN, the objective function in the training process, the existing problems of the original GAN and some mainstream solutions to these problems, and introduces the commonly used model of GAN in the field of earthquake denoising. besides, it summarizes and analyzes the current application and improvement innovation of GAN in the field of seismic denoising, and analyzes the application of GAN in the field of seismic denoising from two aspects of supervised learning and unsupervised learning with examples. Finally, the prospect of GAN for seismic denoising in the future is prospected.},
 year = {2022}
}

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TY  - JOUR
T1  - Research Status and Prospects of Generative Adversarial Networks in Seismic Data Denoising
AU  - Yuan Xu
AU  - Qing Wang
AU  - Shiguang Guo
Y1  - 2022/10/17
PY  - 2022
N1  - https://doi.org/10.11648/j.ijssn.20221002.13
DO  - 10.11648/j.ijssn.20221002.13
T2  - International Journal of Sensors and Sensor Networks
JF  - International Journal of Sensors and Sensor Networks
JO  - International Journal of Sensors and Sensor Networks
SP  - 33
EP  - 50
PB  - Science Publishing Group
SN  - 2329-1788
UR  - https://doi.org/10.11648/j.ijssn.20221002.13
AB  - Efficient and high-quality processing of seismic data collected by geophone sensors is the core of successful seismic exploration. Seismic denoising is a key step in seismic data processing. Traditional seismic denoising relies on manual empirical parameter selection and comparative analysis, which is time-consuming and limited by subjective errors. Deep learning methods based on convolutional neural networks (CNN) have improved the efficiency of denoising massive amounts of seismic data and reduced the manual errors of traditional methods. However, most CNN methods only consider data loss and are weak in recovering the structure of seismic signals, resulting in severe attenuation of some seismic traces in the recovered effective signals, reducing the continuity of seismic events and the quality of seismic data. Generative adversarial networks (GAN), a popular method for deep learning with unique adversarial ideas and powerful feature extraction capabilities, can overcome the limitations of CNN methods in the field of seismic data denoising. This paper firstly introduces the classification and development process of seismic denoising. Then, starting from the principle of GAN, it introduces the workflow of the original GAN, the objective function in the training process, the existing problems of the original GAN and some mainstream solutions to these problems, and introduces the commonly used model of GAN in the field of earthquake denoising. besides, it summarizes and analyzes the current application and improvement innovation of GAN in the field of seismic denoising, and analyzes the application of GAN in the field of seismic denoising from two aspects of supervised learning and unsupervised learning with examples. Finally, the prospect of GAN for seismic denoising in the future is prospected.
VL  - 10
IS  - 2
ER  -

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