Time-Frequency Analysis of Geoacoustic Data Using Adaptive Matching Pursuit

Marapulets, Yu. V.; Lukovenkova, Olga

doi:10.1134/s106377102103009x

Cited by 5 publications

(8 citation statements)

References 15 publications

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“…Additional difficulties arise at this stage. They are associated with a wide variety of pulse waveforms, short duration and strong noisiness by natural and industrial sources [ 14 ]. Thus, the application of time-frequency analysis methods used to solve such problems in allied science fields (Short Time Fourier transform [ 15 ], wavelet transform [ 16 ], wavelet packets [ 17 ] etc.)…”

Section: Acoustic Emission Signal Processingmentioning

confidence: 99%

“…We suggest a new approach to the time-frequency analysis of AE signals. It is based on a sparse approximation method applying the Adaptive Matching Pursuit algorithm [ 14 ]. The main idea of sparse approximation is signal representation in the form of a finite linear combination of functions from some large set linearly dependent in the general case.…”

Section: Acoustic Emission Signal Processingmentioning

confidence: 99%

“…The main disadvantage of the method is its high calculation complexity

. We suggest a modified version of the method, Adaptive Matching Pursuit, which allows us to obtain the solutions of the same accuracy on function sets of smaller sizes [ 14 ].…”

Section: Acoustic Emission Signal Processingmentioning

confidence: 99%

See 2 more Smart Citations

Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects

Marapulets

Solodchuk

Lukovenkova

et al. 2023

Sensors

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Application of acoustic emission of the sound frequency range is under consideration. This range is of current interest for the diagnostics of the stability of mountain slopes, glaciers, ice covers, large technical constructions (bridges, dams, etc.) as well as for the detection of rock deformation anomalies preceding earthquakes. Acoustic sensors, which can be used to record and to determine the directivity of acoustic emission of the sound frequency range, are under consideration. The structure of the system for acoustic emission recording, processing and analysis is described. This system makes it possible to determine the direction to the acoustic emission source using one multi-component sensor. We also consider the algorithms for detection of acoustic emission pulses in a noisy background, and for the analysis of their structure using the Adaptive Matching Pursuit algorithm. A method for the detection of the direction to an acoustic emission signal source based on multi-component sensors is described. The results of application of sound range acoustic emission for the detection of the intensification of rock deformations, associated with earthquake preparation and development in the seismically active region of Kamchatka peninsula, are presented.

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Section: Acoustic Emission Signal Processingmentioning

confidence: 99%

Section: Acoustic Emission Signal Processingmentioning

confidence: 99%

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Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects

Marapulets

Solodchuk

Lukovenkova

et al. 2023

Sensors

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“…The recorded signal analysis results were compared to provide more accurate predictions and analysis of earthquakes. In 2021, Marapulets and Lukovenkova [20] adopted a development method based on the sparse approximation method for time-frequency analysis of geoacoustic data. Through a combined dictionary and adaptive matching pursuit algorithm, the geoacoustic signal was sparsely represented, revealing the seismic time-frequency structural characteristics in front geoacoustic data.…”

Section: Related Workmentioning

confidence: 99%

A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection

He,

Wang,

et al. 2023

Electronics

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Anomaly in geoacoustic emission is an important earthquake precursor. Current geoacoustic anomaly detection methods are limited by their low signal-to-noise ratio, low intensity, sample imbalance, and low accuracy. Therefore, this paper proposes a clone selection algorithm optimized one-class support vector machine method (CSA-OCSVM) for geoacoustic anomaly detection. First, the interquartile range (IQR), cubic spline interpolation, and time window are designed to amplify the geoacoustic signal intensity and energy change rules to reduce the interference of geoacoustic signal noise and intensity. Secondly, to address the imbalance of positive and negative samples in geoacoustic anomaly detection, a one-class support vector machine is introduced for anomaly detection. Meanwhile, in view of the optimization capabilities of the clone selection algorithm, it is adopted to optimize the hyperparameters of OCSVM to improve its detection accuracy. Finally, the proposed model is applied to geoacoustic data anomaly detection in nine different datasets, which are derived from our self-developed acoustic electromagnetic to AI (AETA) system, to verify its effectiveness. By designing comparative experiments with IQR, genetic algorithm OCSVM (GA-OCSVM), particle swarm optimization OCSVM (PSO-OCSVM), and evaluating the performance of the true positive rate (TPR) and false positive rate (FPR), the experimental results depict that the proposed model is superior to the existing state-of-the-art geoacoustic anomaly detection approaches.

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“…It was discovered that the emission maximum before an earthquake is recorded in kilohertz frequency range [6]. It was difficult to make a more detailed analysis of the signal spectrum as long as the recorded AE pulse signals were characterized by a wide diversity of their waveforms and short duration [10]. Thus, the application of time-frequency analysis methods, used to solve such problems in allied sciences (Short Time Fourier transform, wavelet transform, wavelet packets, etc.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Approach to Time-Frequency Analysis of AE Signals of Rocks

Lukovenkova¹,

Marapulets²,

Solodchuk³

2022

Sensors

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The paper describes a new adaptive approach to the investigation of acoustic emission of rocks, the anomalies of which may serve as short-term precursors of strong earthquakes. The basis of the approach is complex methods for monitoring acoustic emission and for analysis of its time-frequency content. Piezoceramic hydrophones and vector receivers, installed at the bottom of natural and artificial water bodies, as well as in boreholes with water, are used as acoustic emission sensors. To perform a time-frequency analysis of geoacoustic signals, we use a sparse approximation based on the developed Adaptive Matching Pursuit algorithm. The application of this algorithm in the analysis makes it possible to adapt to the concrete characteristics of each geoacoustic pulse. Results of the application of the developed approach for the investigation of acoustic emission anomalies, occurring before earthquakes, are presented. We analyzed the earthquakes, that occurred from 2011 to 2016 in the seismically active region of the Kamchatka peninsula, which is a part of the circum-Pacific orogenic belt also known as the “Ring of Fire”. It was discovered that geoacoustic pulse frequency content changes before a seismic event and returns to the initial state after an earthquake. That allows us to make a conclusion on the transformation of acoustic emission source scales before earthquakes. The obtained results may be useful for the development of the systems for environmental monitoring and detection of earthquake occurrences.

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Time-Frequency Analysis of Geoacoustic Data Using Adaptive Matching Pursuit

Cited by 5 publications

References 15 publications

Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects

Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects

A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection

Adaptive Approach to Time-Frequency Analysis of AE Signals of Rocks

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