Multidimensional Seismic Data Denoising Using Framelet-Based Order-<i>p</i> Tensor Deep Learning

Qian, Feng; Wang, Yan; Zheng, Bingwei; Liu, Zhangbo; Zhou, Yingjie; Hu, Guangmin

doi:10.1109/tgrs.2022.3197287

Cited by 5 publications

(4 citation statements)

References 75 publications

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“…In Figure 1b, the highest errors are at traces located next to a noisy trace (marked with red circles). A similar performance can be seen in figure 3 from S. Liu et al (2022). We alleviate these issues by the method proposed in the following section.…”

Section: Blind-trace Denoisingsupporting

confidence: 65%

“…Recent data-driven methods for noise attenuation include dictionary learning (e.g., Nazari Siahsar et al, 2017;X. Wang & Ma, 2019;Zhou et al, 2020;Almadani et al, 2021;Sui et al, 2023), deep learning (e.g., Yu et al, 2019;Zhao et al, 2019;Zhu et al, 2019;Saad & Chen, 2020Wang, Yang, et al, 2022;Farmani et al, 2023;Markovic et al, n. d.) and hybrid methods (e.g., Farmani et al, 2022;Qian et al, 2022;L. Liu & Ma, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…A key requirement for denoising methods is to preserve the signal quality while addressing the denoising task (Ovcharenko and Hou, 2020). In blind-trace denoising (S. Liu et al, 2022), all traces -whether noisy or not -are reconstructed from the neighbouring traces. This results in signal leakage in the entire data and reconstruction errors close to the noisy traces.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, methods that try to separate signal and noise in an alternative domain use Fourier (e.g., Stewart & Schieck, 1989; Hashemi et al., 2008), wavelet (e.g., Mousavi & Langston, 2016; Irani Mehr & Abedi, 2017), curvelet (e.g., Neelamani et al., 2008), seislet (e.g., Fomel & Liu, 2010), Radon (e.g., Trad et al., 2003) and other transforms. Recent data‐driven methods for noise attenuation include dictionary learning (e.g., Nazari Siahsar et al., 2017; X. Wang & Ma, 2019; Zhou et al., 2020; Almadani et al., 2021; Sui et al., 2023), deep learning (e.g., Yu et al., 2019; Zhao et al., 2019; Zhu et al., 2019; Saad & Chen, 2020, 2021; Wang, Yang, et al., 2022; Farmani et al., 2023; Markovic et al., n. d.) and hybrid methods (e.g., Farmani et al., 2022; Qian et al., 2022; L. Liu & Ma, 2023). The deep learning methods either directly attenuate the noise or detect the noise for other noise attenuation algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Abedi,

Pardo,

Alkhalifah

2023

Geophysical Prospecting

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Trace‐wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self‐supervised deep learning to attenuate this type of noise, the conventional blind‐trace deep learning trains a network to blindly reconstruct each trace in the data from its surrounding traces; it attenuates isolated trace‐wise noise but causes signal leakage in clean and noisy traces and reconstruction errors next to each noisy trace. To reduce signal leakage and improve denoising, we propose a new loss function and masking procedure in a semi‐blind‐trace deep learning framework. Our hybrid loss function has weighted active zones that cover masked and non‐masked traces. Therefore, the network is not blinded to clean traces during their reconstruction. During training, we dynamically change the masks' characteristics. The goal is to train the network to learn the characteristics of the signal instead of noise. The proposed algorithm enables the designed U‐net to detect and attenuate trace‐wise noise without having prior information about the noise. A new hyperparameter of our method is the relative weight between the masked and non‐masked traces' contribution to the loss function. Numerical experiments show that selecting a small value for this parameter is enough to significantly decrease signal leakage. The proposed algorithm is tested on synthetic and real off‐shore and land datasets with different noises. The results show the superb ability of the method to attenuate trace‐wise noise while preserving other events. An implementation of the proposed algorithm as a Python code is also made available.This article is protected by copyright. All rights reserved

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Section: Blind-trace Denoisingsupporting

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Abedi,

Pardo,

Alkhalifah

2023

Geophysical Prospecting

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Combined multi-branch selective kernel hybrid-pooling skip connection residual network for seismic random noise attenuation

Zeng

Zhang

Yong

et al. 2022

Journal of Geophysics and Engineering

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To improve the generalization ability of the single pooling (average or maximum pooling) skip connection residual network (SSN) for seismic random noise attenuation, we present a hybrid-pooling skip connection residual network (HSN). In HSN, the hybrid pooling consists of average and maximum pooling and aims to simultaneously capture the local and global features well, ultimately improving the detail recovery capability of HSN. To further improve the network performance and denoising ability of HSN, we propose a combined multi-branch selective kernel (CSK) hybrid-pooling skip connection residual network, which is referred to as CHSN. In CHSN, CSK consists of a three-branch selective kernel (TSK) and our suggested four-branch selective kernel (FSK), and aims to adaptively capture feature maps for high-accuracy effective information recovery. The superior random noise attenuation ability of CHSN is demonstrated in both synthetic three- and actual two-dimensional seismic data.

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FedTADBench: Federated Time-series Anomaly Detection Benchmark

Liu

Zeng

Zhang

et al. 2022

2022 IEEE 24th Int Conf on High Performance Computing &Amp; Communications; 8th Int Conf on Data Science &Amp; Systems; 20th In

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Multidimensional Seismic Data Denoising Using Framelet-Based Order-p Tensor Deep Learning

Cited by 5 publications

References 75 publications

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Combined multi-branch selective kernel hybrid-pooling skip connection residual network for seismic random noise attenuation

FedTADBench: Federated Time-series Anomaly Detection Benchmark

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