Articles | Volume 16, issue 6
https://doi.org/10.5194/os-16-1367-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-1367-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Nov 2020
Research article |
| 11 Nov 2020
| 11 Nov 2020
Random noise attenuation of sparker seismic oceanography data with machine learning
Hyunggu Jun
Marine active fault research center, Korea Institute of Ocean Science & Technology, Busan, 49111,
Republic of Korea
Hyeong-Tae Jou
CORRESPONDING AUTHOR
Marine active fault research center, Korea Institute of Ocean Science & Technology, Busan, 49111,
Republic of Korea
Chung-Ho Kim
Marine active fault research center, Korea Institute of Ocean Science & Technology, Busan, 49111,
Republic of Korea
Sang Hoon Lee
Marine active fault research center, Korea Institute of Ocean Science & Technology, Busan, 49111,
Republic of Korea
Han-Joon Kim
Marine active fault research center, Korea Institute of Ocean Science & Technology, Busan, 49111,
Republic of Korea
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Cited
18 citations as recorded by crossref.
- Study on the Seismic Random Noise Attenuation for the Seismic Attribute Analysis J. Won et al. 10.9719/EEG.2024.57.1.51
- Repeatability enhancement of time-lapse seismic data via a convolutional autoencoder H. Jun & Y. Cho 10.1093/gji/ggab397
- Resolution enhancement for a seismic velocity model using machine learning S. Kim et al. 10.1093/gji/ggae169
- Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation L. Guo et al. 10.1109/ACCESS.2022.3147242
- Progress and prospects of seismic oceanography H. Song et al. 10.1016/j.dsr.2021.103631
- DECIPHERING THE DEEP: MACHINE LEARNING APPROACHES TO UNDERSTANDING OCEANIC ECOSYSTEMS T. Miller et al. 10.36074/grail-of-science.16.02.2024.093
- Turbulent Heat Fluxes in a Mediterranean Eddy Quantified Using Seismic and Hydrographic Observations W. Xiao & Z. Meng 10.3390/jmse10060720
- Nonrepeatable Noise Attenuation on Time-Lapse Prestack Data Using Fully Convolutional Neural Network and Masked Image-to-Image Translation Scheme J. Lee et al. 10.1109/TGRS.2024.3460767
- REWARE: a seismic processing algorithm to retrieve geological information from the water column R. Sylvain et al. 10.1093/gji/ggae319
- Mid-Ocean Ridge and Storm Enhanced Mixing in the Central South Atlantic Thermocline J. Wei et al. 10.3389/fmars.2021.771973
- Application of Machine-Learning for Detecting Gas Indicator Distribution from Seismic Data J. Won & H. Jun 10.9719/EEG.2024.57.6.681
- Cross‐equalization for time‐lapse sparker seismic data S. Lee et al. 10.1111/1365-2478.13600
- Denoising sparker seismic data with Deep BiLSTM in fractional Fourier transform D. Lee et al. 10.1016/j.cageo.2024.105519
- Reviving Legacy Seismic Data via Machine Learning Technique—Part 1: Expanding 3-D Seismic Survey Coverage With Gated Convolution GAN J. Lee et al. 10.1109/TGRS.2024.3524572
- Loss functions in machine learning for seismic random noise attenuation H. Jun & H. Kim 10.1111/1365-2478.13449
- Nonlinear Perception Characteristics Analysis of Ocean White Noise Based on Deep Learning Algorithms T. Qian et al. 10.3390/math12182892
- Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise Modeling C. Meng et al. 10.1109/TGRS.2022.3175535
- A simple model for evaluating the performance of sparker source with multi-electrode array L. Zhang et al. 10.1063/5.0211859
18 citations as recorded by crossref.
- Study on the Seismic Random Noise Attenuation for the Seismic Attribute Analysis J. Won et al. 10.9719/EEG.2024.57.1.51
- Repeatability enhancement of time-lapse seismic data via a convolutional autoencoder H. Jun & Y. Cho 10.1093/gji/ggab397
- Resolution enhancement for a seismic velocity model using machine learning S. Kim et al. 10.1093/gji/ggae169
- Seismic Random Noise Removal Based on a Multiscale Convolution and Densely Connected Network for Noise Level Evaluation L. Guo et al. 10.1109/ACCESS.2022.3147242
- Progress and prospects of seismic oceanography H. Song et al. 10.1016/j.dsr.2021.103631
- DECIPHERING THE DEEP: MACHINE LEARNING APPROACHES TO UNDERSTANDING OCEANIC ECOSYSTEMS T. Miller et al. 10.36074/grail-of-science.16.02.2024.093
- Turbulent Heat Fluxes in a Mediterranean Eddy Quantified Using Seismic and Hydrographic Observations W. Xiao & Z. Meng 10.3390/jmse10060720
- Nonrepeatable Noise Attenuation on Time-Lapse Prestack Data Using Fully Convolutional Neural Network and Masked Image-to-Image Translation Scheme J. Lee et al. 10.1109/TGRS.2024.3460767
- REWARE: a seismic processing algorithm to retrieve geological information from the water column R. Sylvain et al. 10.1093/gji/ggae319
- Mid-Ocean Ridge and Storm Enhanced Mixing in the Central South Atlantic Thermocline J. Wei et al. 10.3389/fmars.2021.771973
- Application of Machine-Learning for Detecting Gas Indicator Distribution from Seismic Data J. Won & H. Jun 10.9719/EEG.2024.57.6.681
- Cross‐equalization for time‐lapse sparker seismic data S. Lee et al. 10.1111/1365-2478.13600
- Denoising sparker seismic data with Deep BiLSTM in fractional Fourier transform D. Lee et al. 10.1016/j.cageo.2024.105519
- Reviving Legacy Seismic Data via Machine Learning Technique—Part 1: Expanding 3-D Seismic Survey Coverage With Gated Convolution GAN J. Lee et al. 10.1109/TGRS.2024.3524572
- Loss functions in machine learning for seismic random noise attenuation H. Jun & H. Kim 10.1111/1365-2478.13449
- Nonlinear Perception Characteristics Analysis of Ocean White Noise Based on Deep Learning Algorithms T. Qian et al. 10.3390/math12182892
- Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise Modeling C. Meng et al. 10.1109/TGRS.2022.3175535
- A simple model for evaluating the performance of sparker source with multi-electrode array L. Zhang et al. 10.1063/5.0211859
Latest update: 23 Apr 2025
Short summary
Seismic oceanography acquires water column reflections by seismic exploration. The use of a high-frequency seismic source such as sparker can enhance the resolution of reflection image but suffers from a low signal-to-noise ratio problem. In this study, we applied a machine learning to remove the random noise in water column seismic section. We constructed appropriate training data and showed that the machine learning can successfully remove the random noise in the water column seismic section.
Seismic oceanography acquires water column reflections by seismic exploration. The use of a...
