Econ. Environ. Geol. 2024; 57(5): 499-512
Published online October 29, 2024
https://doi.org/10.9719/EEG.2024.57.5.499
© THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY
Correspondence to : *wansooha@pknu.ac.kr
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided original work is properly cited.
Random noise in seismic data can significantly impair hydrocarbon exploration by degrading the quality of subsurface imaging. We propose a deep learning approach to attenuate random noise in Laplace-domain seismic wavefields. Our method employs a modified U-Net architecture, trained on diverse synthetic P-wave velocity models simulating the Gulf of Mexico subsurface. We rigorously evaluated the network’s denoising performance using both the synthetic Pluto velocity model and real Gulf of Mexico field data. We assessed the effectiveness of our approach through Laplace-domain full waveform inversion. The results consistently show that our U-Net approach outperforms traditional singular value decomposition methods in noise attenuation across various scenarios. Numerical examples demonstrate that our method effectively attenuates random noise and significantly enhances the accuracy of subsequent seismic imaging processes.
Keywords seismic data processing, deep learning, random noise attenuation, Laplace domain, full waveform inversion
Econ. Environ. Geol. 2024; 57(5): 499-512
Published online October 29, 2024 https://doi.org/10.9719/EEG.2024.57.5.499
Copyright © THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY.
Lydie Uwibambe, Jun Hyeon Jo, Wansoo Ha*
Department of Energy Resources Engineering, Pukyong National University, Busan 48513, South Korea
Correspondence to:*wansooha@pknu.ac.kr
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided original work is properly cited.
Random noise in seismic data can significantly impair hydrocarbon exploration by degrading the quality of subsurface imaging. We propose a deep learning approach to attenuate random noise in Laplace-domain seismic wavefields. Our method employs a modified U-Net architecture, trained on diverse synthetic P-wave velocity models simulating the Gulf of Mexico subsurface. We rigorously evaluated the network’s denoising performance using both the synthetic Pluto velocity model and real Gulf of Mexico field data. We assessed the effectiveness of our approach through Laplace-domain full waveform inversion. The results consistently show that our U-Net approach outperforms traditional singular value decomposition methods in noise attenuation across various scenarios. Numerical examples demonstrate that our method effectively attenuates random noise and significantly enhances the accuracy of subsequent seismic imaging processes.
Keywords seismic data processing, deep learning, random noise attenuation, Laplace domain, full waveform inversion
Table 1 . MSE losses of the noisy and denoised Pluto data calculated with the clean data.
Data | MSE loss |
---|---|
Noisy data | 0.5870 |
Denoised (SVD) | 0.0935 |
Denoised (U-Net) | 0.0372 |
Table 2 . MSE losses of the logarithmic forward-modeled data generated from the inversion results calculated with the observed Gulf of Mexico data.
Data of FWI | MSE loss |
---|---|
Original noisy data | 2.6636 |
Denoised (SVD) | 2.4736 |
Denoised (U-Net) | 1.9878 |
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