A Review of Variational Mode Decomposition in Seismic Data Analysis

Liu, Wei; Liu, Yang; Li, Shuangxi; Chen, Yangkang

doi:10.1007/s10712-022-09742-z

Cited by 23 publications

(14 citation statements)

References 84 publications

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“…T orig prep + T orig train + S orig S test T test proc (10) where T orig prep , T orig train and S orig denote the T prep , T train and S of the original zone. The T proc for original zone can be approximately regarded as S orig S test T test proc .…”

Section: A Data Analysismentioning

confidence: 99%

“…This is because these methods often blend a mixture of noise and valid data, resulting in residual noise. Modal decomposition methods [10]- [13] aim to improve signal reconstruction and reduce random noise by decomposing seismic data into multiple components and superimposing primary components representing clean data. However, the clean data and noise can be inevitablely mixed during this decomposition process.…”

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confidence: 99%

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Self-Supervised Attenuation Method Based on Similarity Comparisons for 3D Seismic Random Noise

Gao,

Gao

2024

IEEE Access

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The improvement of the signal-to-noise ratio of seismic data is crucial for high-precision processing. The self-supervised denoising methods based on correlation differences are gaining attention due to their low cost of training data construction and the ability to build training data directly on test data. However, as the volume of data processed increases, these methods must maintain a denoising effect by adding extra processings. This increase in processing times can lead to a rise in total cost, making these methods less cost-effective compared to other deep learning methods for processing large data. Therefore, we improved these self-supervised methods by altering the training data construction process, thereby retaining its cost advantage. Specifically, we modified the selection process of zones used for training data construction. Through correlation analysis, these methods can obtain higher-quality zones from the original zone for training, indicating that the network needs to be trained only once to process any data in the original zone. Without the requirement for additional processing to ensure noise attenuation, these selfsupervised methods can maintain their cost advantage when processing large data. We applied one of these self-supervised methods to synthetic and field examples to demonstrate the enhancement's effectiveness. Experimental results show that the improved method performs as well as the conventional self-supervised method in suppressing random noise and constructing reflection events but with a significant cost advantage.

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Section: A Data Analysismentioning

confidence: 99%

mentioning

confidence: 99%

Self-Supervised Attenuation Method Based on Similarity Comparisons for 3D Seismic Random Noise

Gao,

Gao

2024

IEEE Access

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“…The use of the complex conjugate of the signal in the autocorrelation accounts for the magnitude and phase parts of the signal. Note that the traditional autocorrelation function of (20) integrates the autocorrelation over time t, providing a global measure of the correlation. In contrast, the IACF provides a local measure of the correlation, which is better suited to reflect local and time-varying features of the signal.…”

Section: G Wigner-ville Distributionmentioning

confidence: 99%

“…The study analyzed numerous articles related to EEG signal processing, identified limitations, and analyzed future development trends. Besides biomedical and BdM fields, the role of signal processing in ML has been addressed in various fields such as audio analysis and recognition [14]- [20], seismic signal analysis [20]- [22], and telecommunications [23]- [30].…”

Section: Introductionmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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“…Ambient noise and instrument-related interference can complicate the recorded waveforms. Variational Mode Decomposition (VMD), a non-recursive signal processing method developed in 2014 was utilized to address this (Dragomiretskiy and Zosso, 2013;Liu et al, 2016;Liu et al, 2023). VMD is renowned for its effective noise reduction capabilities and its ability to mitigate the issue of frequency aliasing commonly seen in empirical mode decomposition (EMD).…”

Section: Characteristics Of Strong Mine Tremorsmentioning

confidence: 99%

Propagation and impacts on roadway of mining-induced far-field strong tremors: insights from numerical simulations

Han,

Dou,

Gong

et al. 2024

Front. Earth Sci.

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Mining-induced far-field mine tremors, which often cause strong ground tremors, are receiving more attention due to their increasing occurrence. Investigating the rock burst risk of roadway caused by those tremors is crucial to ensure production safety. In this study, Variational Mode Decomposition was used to investigate the wave characteristics of strong mine tremors. The propagation and attenuation of these tremors were explored using the dynamic analysis of Flac3d. The amplification factor was introduced to assess the impact of these tremors on roadways. Plastic zone volume increment and Brittle Shear Ratio (BSR) are used to assess the roadway failure and the rock burst potential caused by these tremors, respectively. The main findings are as follows: 1) Compared with the main frequency of near-field mine tremor waveform, the far-field mine tremors waveform are mainly low frequency below 5 Hz; 2) In the simulation, peak vibration velocities of P-wave and S-wave follow a power-law decay as the propagation distance increases, with P-wave attenuating faster than S-wave; 3) Under similar conditions, P-wave induce higher vibration velocities than S-wave, but S-wave generally exhibit a greater amplification factor than P-wave; 4) When the direction of dynamic load is consistent with the direction of maximum principal stress, the rock burst potential of roadway is higher, which explains the phenomenon that the rock burst potential of roadway under S-wave loading is higher than that under P-wave.

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A Review of Variational Mode Decomposition in Seismic Data Analysis

Cited by 23 publications

References 84 publications

Self-Supervised Attenuation Method Based on Similarity Comparisons for 3D Seismic Random Noise

Self-Supervised Attenuation Method Based on Similarity Comparisons for 3D Seismic Random Noise

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Propagation and impacts on roadway of mining-induced far-field strong tremors: insights from numerical simulations

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