Marine seismic signal denoising using VMD with Hausdorff distance and wavelet transform

Kesharwani, Abhishek; Aggarwal, Vaibhav; Singh, Shubham; R, Rahul B; Kumar, Arvind

doi:10.1177/15485129211036044

Cited by 4 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, common methods for identifying effective microseismic signals include parametric analysis, waveform analysis, wavelet transform, pattern recognition, etc. But most of these methods require manual processing, which is subject to the unstable classification efficiency and affected by the prior experience of processors [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Shen,

Wang,

Zeng

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to-noise ratio, a method for identifying effective microseismic signals in a strong-noise environment by using the variational mode decomposition (VMD) and genetic algorithm (GA)-based optimized support vector machine (SVM) model is proposed. Microseismic signals with a low signal-to-noise ratio are adaptively decomposed into several intrinsic mode functions (IMFs) by using VMD. The characteristics of such IMFs are extracted and used as a basis for the determination of signal validity. The SVM model is optimized by utilizing GA to obtain the optimal penalty factor c and the kernel function parameter g. The availability of IMF components is judged by the optimized SVM model, based on which the effectiveness of microseismic signals is further identified. By applying the algorithm to the microseismic signals with artificially added noise, the effective microseismic signals and ineffective noise are discriminated, verifying the feasibility of the algorithm. After processing the microseismic records collected in the field, we effectively judge the effectiveness of microseismic signals, suppress the interfering noise in the data and greatly improve the signal-to-noise ratio of the seismic records. The results show that the method for identifying effective microseismic signals based on VMD and GA-SVM can well discriminate between effective and ineffective microseismic signals, which is very significant and provides technical support for microseismic monitoring in a strong-noise environment.

show abstract

Section: Introductionmentioning

confidence: 99%

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Shen,

Wang,

Zeng

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, the above methods cannot handle the complex DAS background noise. In addition, multi-scale denoising methods use the features of the sparse decomposition results to construct suitable filters for the purpose to suppress the noise to retain the effective signal, and typical methods include wavelet transform filtering (Mousavi et al, 2016;Anvari et al, 2017), Curvelet transform filtering (Neelamani et al, 2008;Gorszczyk et al, 2014), Shearlet transform filtering (Gan et al, 2015;Chen and Fomel 2018), empirical mode decomposition (EMD) (Bekara and van der Baan, 2009;Amezquita Sanchez et al, 2017) and variational modal decomposition (VMD) (Kesharwani et al, 2021). Unfortunately, when dealing with DAS recordings containing complex noise, researchers have difficulty in obtaining optimal filtering parameters, which leads to noise residuals and loss of amplitude of the effective signal.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale interactive network in the application of DAS seismic data processing

Wang

Lin²,

Shao³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

Distributed acoustic sensing (DAS) is regarded as a novel acquisition technology for seismic data. Compared with conventional electrical geophones, DAS has a series of obvious advantages including low-cost, high spatial resolution, good coverage, and strong resistance to the harsh environment. Noise attenuation is an essential step in seismic data processing. However, there are two main difficulties faced by the denoising task of DAS seismic data. On the one hand, some background noise in DAS seismic data, such as optical low-frequency noise, horizontal noise, and fading noise, is unique and not presented in the conventional seismic data; on the other hand, the signal-to-noise ratio (SNR) of DAS seismic data is relatively low. Recently, a convolutional neural network (CNN) has shown superior denoising performance compared to the traditional method. To follow this promising trend, we propose a multi-scale interactive convolutional neural network (MSI-Net) and apply it to denoise the challenging DAS seismic data. Different from most of the existing CNN architecture used in seismic data denoising, the MSI-Net considers both coarse-scale and fine-scale features by improving the inherent serial convolution to multi-scale parallel convolution, which is beneficial to recover detailed information. Moreover, we utilize some connections to achieve the information interaction between different scales, which promotes the flow of information and enables the network to extract more informative multi-scale features from the DAS seismic data. Moreover, both synthetic and real examples demonstrate that the proposed MSI-Net can effectively attenuate a variety of unique DAS background noise and also completely recover the weak signals. Compared with conventional CNN architecture, MSI-Net exhibits better performance in global SNR and local details.

show abstract

“…So far, researchers have come up with many methods to suppress random noise. Common methods include predictive filtering (Chen and Ma, 2014;Liu et al, 2020;Wang et al, 2021), mode decomposition (Han and van der Baan, 2015;Gómez and Velis, 2016;Long et al, 2021), low-rank constraints (Anvari et al, 2017;Huang, 2022), and transform domain (Kesharwani et al, 2022;Xie et al, 2022;Zhang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Prestack seismic random noise attenuation using the wavelet-inspired invertible network with atrous convolutions spatial pyramid

Wu²,

Xu³

2023

Front. Earth Sci.

View full text Add to dashboard Cite

Convolutional Neural Network (CNN) is widely used in seismic data denoising due to its simplicity and effectiveness. However, traditional seismic denoising methods based on CNN ignore multi-scale features of seismic data in the wavelet domain. The lack of these features will decrease the accuracy of denoising results. To address this barrier, a seismic denoise method based on the wavelet-inspired invertible network with atrous convolutions spatial pyramid (WINNet_ACSP) is proposed. WINNet_ACSP follows the principle of lifting wavelet transform. The proposed method utilizes the redundant orthogonal wavelet transform to obtain frequency multi-scale information from noisy seismic data. Then predict update network (PUNet) extracts spatial multi-scale features of approximate and detailed parts. The sparse driven network (SDN) learns the complex multi-scale information and obtains sparse features. These sparse features are processed to eliminate random noise. Compared to standard convolution, the atrous convolutions spatial pyramid (ACSP) can extract more features. The redundant features are the key to ensure the precision of multi-scale information. Therefore, the introduction of ACSP in PUNet can guarantee the denoising effect of the network. WINNet_ASCP combines the characteristics of wavelet transform and neural network and has a high generalization. Besides, transfer learning is used to overcome the difficulty caused by the training sample size of seismic data. The training process includes pre-training and post-training. The former is trained to obtain the initial denoising network by natural image samples. The latter is trained with a small sample of seismic data to enhance stratigraphic continuity. Finally, the proposed method is tested with synthetic and field data. The experimental results show that the proposed method can effectively remove random noise and reduce the loss of detailed information in prestack seismic data. In the future, we will make further improvements on this basis and conduct experiments on 3D prestack data.

show abstract

Marine seismic signal denoising using VMD with Hausdorff distance and wavelet transform

Cited by 4 publications

References 28 publications

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Multi-scale interactive network in the application of DAS seismic data processing

Prestack seismic random noise attenuation using the wavelet-inspired invertible network with atrous convolutions spatial pyramid

Contact Info

Product

Resources

About