Machine-learning Based Noise Attenuation of Field Seismic Data using Noise Data Acquisition

Jun, Hyoung Oh; Kim, Chung-Ho; Kim, Han Joon

doi:10.32390/ksmer.2021.58.5.408

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Zhao et al, 2019;Yuan et al, 2020). In addition, there are several studies related to the construction of proper training data for supervised learning (Jun et al, 2020(Jun et al, , 2021 and the development of unsupervised learning-based noise attenuation models (Saad & Chen, 2021;X. Zhang et al, 2021;Gao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…H. Zhao et al (2016) showed that the use of a mixed loss function, which was the 𝑙 1 norm combined with the SSIM (Wang et al, 2004) or multiscale SSIM (MS-SSIM) (Wang et al, 2003), could generate better denoising results than the use of a single loss function. Following H. Zhao et al (2016), Kim et al (2021 performed noise attenuation with the loss function combining the 𝑙 1 norm and MS-SSIM and compared the denoised result with that of the single 𝑙 2 norm. However, the use of multiple losses essentially requires a proper balance between the losses because the weight of each loss has a considerable impact on the training; this represents a large obstacle to the use of multiple losses (Kendall et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The major research topics of interest include defining or improving the network architecture (Saad & Chen, 2020; Yang, Chen et al., 2021; Sun et al., 2022; Jun & Cho, 2022) and attenuating certain types of noise (X. Zhao et al., 2019; Yuan et al., 2020). In addition, there are several studies related to the construction of proper training data for supervised learning (Jun et al., 2020, 2021) and the development of unsupervised learning‐based noise attenuation models (Saad & Chen, 2021; Yang, Wang et al., 2021; X. Zhang et al., 2021; Gao et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

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Loss functions in machine learning for seismic random noise attenuation

Jun,

Kim

2023

Geophysical Prospecting

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Seismic random noise is one of the main factors that degrade the quality of seismic data. Therefore, seismic random noise attenuation should be performed appropriately through several stages during seismic data processing, and this requires sufficient experience and knowledge because the proper hyperparameters need to be determined based on the features of the noise in the target seismic data. Recently, machine learning‐based seismic noise attenuation has been widely studied because it provides suitable results by learning noise features from noisy data, unlike conventional physics‐based approaches. There are many important factors in the machine learning, and here, we focus on the loss functions of machine learning in terms of seismic random noise attenuation. The most widely used loss function is l2, but we train a model with various kinds of single and multiple loss functions and attenuate seismic random noise. We analyze the efficiency of loss functions by comparing the noise attenuated results of synthetic and field seismic data qualitatively and quantitatively. Our analysis indicates that the multiple loss function with l1 norm can be a proper choice for random noise suppression of seismic data.This article is protected by copyright. All rights reserved

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Loss functions in machine learning for seismic random noise attenuation

Jun,

Kim

2023

Geophysical Prospecting

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Cross‐equalization for time‐lapse sparker seismic data

Lee,

Won,

Jun

2024

Geophysical Prospecting

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Time‐lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time‐lapse seismic exploration has been conducted using a large‐scale exploration system. However, for efficient monitoring of shallow subsurface, time‐lapse monitoring based on the small‐scale exploration system is required. Small‐scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal‐to‐noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small‐scale time‐lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre‐stack, post‐stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre‐stack data processing stage. In the post‐stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high‐resolution time‐lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.

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Machine-learning Based Noise Attenuation of Field Seismic Data using Noise Data Acquisition

Cited by 2 publications

References 9 publications

Loss functions in machine learning for seismic random noise attenuation

Loss functions in machine learning for seismic random noise attenuation

Cross‐equalization for time‐lapse sparker seismic data

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