Time‐frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit

Semnani, Amir; Wang, Liang; Ostadhassan, Mehdi; Nabi-Bidhendi, Majid; Araabi, Babak Nadjar

doi:10.1111/1365-2478.12767

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…The quality of decomposition was evaluated using the formula for cross‐correlation ( CC ) between the original seismic trace and the reconstructed trace (Semnani et al ., 2019):

\begin{matrix} true \\ right C C = \frac{\sum_{n = 1}^{N} ((), f ((), n) - μ_{f}) ((), \overset{f}{true} ((), n) - μ_{\hat{f}})}{\sqrt{\sum_{n = 1}^{N} {(f (n) - μ_{f})}^{2}} \sqrt{\sum_{n = 1}^{N} {(\hat{f} (n) - μ_{\overset{f}{true}})}^{2}}} \times 100 %, \end{matrix}

where

f (n) normalis normalthe normaloriginal normalsignal

\hat{f} false(n false)

denotes the reconstructed signal,

μ_{f}

and

μ_{\hat{f}}

represent the mean values of f(n) and

\hat{f} false(n false)

respectively, and N is the number of signal samples.…”

Section: Matching Pursuit Decomposition With Gabor Atomsmentioning

confidence: 99%

Cable reverberations during wireline distributed acoustic sensing measurements: their nature and methods for elimination

Martuganova

Stiller

Bauer

et al. 2021

Geophysical Prospecting

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The application of distributed acoustic sensing in borehole measurements allows for the use of fibre optic cables to measure strain. This is more efficient in terms of time and costs compared with the deploying of conventional borehole seismometers. Nevertheless, one known drawback for temporary deployment is represented by the freely hanging wireline cable slapping and ringing inside the casing, which introduces additional coherent coupling noise to the data. The present study proposes an explanation for the mechanism of noise generation and draws an analogy with similar wave propagation processes and phenomena, such as ghost waves in marine seismics. This observation allows to derive a ringing noise filter function, to study its behaviour and to consider known effects of the gauge length filter. After examining existing methods aimed at eliminating ringing noise and results of their application, we propose a two‐step approach: (1) developing a denoising method based on a matching pursuit decomposition with Gabor atoms and (2) subtracting the noise model for imaging improvement. The matching pursuit method focuses on decomposing the original input signal into a weighted sum of Gabor functions. Analysing Gabor atoms properties for frequency, amplitude and position in time provides the opportunity to distinguish parts of the original signal denoting noise caused by the vibrating cable. The matching pursuit decomposition applied to the distributed acoustic sensing‐vertical seismic profiling data at the geothermal test site Groß Schönebeck provides a versatile processing instrument for noise suppression.

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“…The quality of decomposition was evaluated using the formula for cross‐correlation ( CC ) between the original seismic trace and the reconstructed trace (Semnani et al ., 2019):

\begin{matrix} true \\ right C C = \frac{\sum_{n = 1}^{N} ((), f ((), n) - μ_{f}) ((), \overset{f}{true} ((), n) - μ_{\hat{f}})}{\sqrt{\sum_{n = 1}^{N} {(f (n) - μ_{f})}^{2}} \sqrt{\sum_{n = 1}^{N} {(\hat{f} (n) - μ_{\overset{f}{true}})}^{2}}} \times 100 %, \end{matrix}

where

f (n) normalis normalthe normaloriginal normalsignal

\hat{f} false(n false)

denotes the reconstructed signal,

μ_{f}

and

μ_{\hat{f}}

represent the mean values of f(n) and

\hat{f} false(n false)

respectively, and N is the number of signal samples.…”

Section: Matching Pursuit Decomposition With Gabor Atomsmentioning

confidence: 99%

Cable reverberations during wireline distributed acoustic sensing measurements: their nature and methods for elimination

Martuganova

Stiller

Bauer

et al. 2021

Geophysical Prospecting

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show abstract

“…Li & Zhang (2017) extended the parallel computation by considering the number of envelope peaks versus the number of computer nodes. Semnani et al (2019) introduced quantum swarm evolutionary matching pursuit to boost the efficiency of the conventional matching pursuit.…”

Section: Introductionmentioning

confidence: 99%

Structure-adapted Multichannel Matching Pursuit for Seismic Trace Decomposition

Zhao

Rao

Wang

2023

Pure Appl. Geophys.

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While the single-channel matching pursuit decomposes a seismic trace into a series of wavelets, the multi-channel matching pursuit examines the lateral coherence of the seismic traces as a constraint to improve the lateral continuity of the decomposition. However, the presence of structures in the subsurface negatively affects the performance of the multi-channel matching pursuit. We proposed a structure-adapted matching pursuit method that uses a dynamic time-warping (DTW) algorithm to estimate the similarity between adjacent seismic traces and extract an optimal wavelet along the dip plane. This structure-adapted implementation would significantly speed up the convergence of the decomposition process.We modified the DTW algorithm by combining the Euclidean distance of the seismic trace and the first-order temporal derivative of the seismic trace. We also updated the amplitudes of all extracted wavelets simultaneously using the least-squares principle. This DTW-based, structure-adapted, multi-channel, least-squares matching pursuit would improve the robustness and accuracy of the seismic trace decomposition.

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“…Therefore, selecting the appropriate family type and DWT decomposition level for the feature representation of a class is important because these two parameters affect the classification performance ( [28]; [29]; [30]; [26]; [31]). Some studies used metaheuristic methods to optimise the selection of family types and DWT decomposition levels, such as genetic algorithms ( [32]; [33]), particle swarm optimisation ( [34]; [26], [32]), whale optimisation algorithm [35], and evolutionary quantum swarm algorithm [36]. Many researchers used metaheuristic methods to obtain the optimal wavelet family and the decomposition level for research in engineering fields.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

IJMA

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Features play an important role in representing classes in the hierarchy structure, and using unsuitable features will affect classification performance. The discrete wavelet transform (DWT) approach provides the ability to create the appropriate features to represent data. DWT can produce global and local features using different wavelet families and decomposition levels. These two parameters are essential to obtain a suitable representation for classes in the hierarchy structure. This study proposes using a particle swarm optimisation (PSO) algorithm to select the suitable wavelet family and decomposition level for G-protein coupled receptor (GPCR) hierarchical class representation. The results indicate that the PSO algorithm mostly selects Biorthogonal wavelets and decomposition level 2 to represent GPCR protein. Concerning the performance, the proposed method achieved an accuracy of 97.9%, 85.9%, and 77.5% at the family, subfamily, and subsubfamily levels, respectively.

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Time‐frequency decomposition of seismic signals via quantum swarm evolutionary matching pursuit

Cited by 11 publications

References 33 publications

Cable reverberations during wireline distributed acoustic sensing measurements: their nature and methods for elimination

Cable reverberations during wireline distributed acoustic sensing measurements: their nature and methods for elimination

Structure-adapted Multichannel Matching Pursuit for Seismic Trace Decomposition

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