Discrimination of earthquakes and explosions using multi-fractal singularity spectrums properties

Lyubushin, A. A.; Kaláb, Zdeněk; Lednická, Markéta; Haggag, Hamed Mohamed

doi:10.1007/s10950-013-9366-3

Cited by 17 publications

(11 citation statements)

References 12 publications

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“…The peak acceleration in each of these directions is recorded. In earthquake research and engineering practice, the singularity spectrum is the standard tool for analyzing the signal [2,3]. These multiscale techniques are well developed and have been applied in a variety of fields [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…The maximum ground movement observed along the fault was recorded at 7 m vertical displacement. The spatial and temporal complexity of the earthquake source is strongly affected by spatial heterogeneities of fault strength and stress, and has a major impact on the amplitude and spatial variability of ground motions in the near-field sites [2,3]. Selfsimilarity arguments suggest that this complexity extends over a broad range of length scales.…”

Section: Introductionmentioning

confidence: 99%

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Multifractal analysis for seismic wave in Kathmandu valley after Gorkha Earthquake-2015, Nepal

Tiwari

Adhikari

et al. 2020

J. Nep. Phys. Soc.

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We applied the multiscale signal processing technique, the Wavelet Transform Modulus Maxima (WTMM) to characterize high frequency properties of strong motion waveforms, in particular the temporal distribution and strength of singularities in Gorkha earthquake, 25th April 2015. We first explored their relation to earthquake data source. Then we applied the WTMM analysis to strong motion recordings. These showed that the timing and exponent of singularities measured by the WTMM method on the ground motion wave field are directly related to the position and exponent of assumed initial stress singularities on the fault plane. We found strong motion recordings at near the epicenter site have very high multifractality than far sites. Some differences and similarities among sites were successfully detected.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multifractal analysis for seismic wave in Kathmandu valley after Gorkha Earthquake-2015, Nepal

Tiwari

Adhikari

et al. 2020

J. Nep. Phys. Soc.

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“…Discrimination analysis was used by Che et al [5] to identify an explosion-induced event in North Korea. In addition, Lyubushin et al [6] classified seismic records acquired from the Aswan Dam region in Egypt as either natural events or blasts using the spectral support widths method. Rouet-Leduc et al [7] investigated laboratory earthquakes to predict the failure time of a fault based on acoustical information and an ML method called random forest (RDF) classification.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective

Karimzadeh

Matsuoka

Kuang

et al. 2019

IJGI

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Small earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary (“yes” or “no”) logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis.

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“…Statistical time series classifier based on hidden Markov model tool was introduced in Quang et al (2015) and Beyreuther et al (2012). In Lyubushin et al (2013), multi-fractal singularity spectral was used to extract some features which can characterize earthquakes and quarry blasts, while the authors in Kortström et al (2016) adopted support vector machine (SVM) for discrimination. They filtered the seismic wave via many narrow band pass filters and divided them into four phase windows: P, Pcoda, S, and Scoda, then computed a short-term average (STA) to use them for training the SVM.…”

Section: Introductionmentioning

confidence: 99%

Automatic discrimination of earthquakes and quarry blasts using wavelet filter bank and support vector machine

2018

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False discrimination between earthquakes and quarry blasts may lead to an unrealistic characterization of the natural seismicity of a region. The similarity in seismograms between earthquakes and quarry blasts is the primary reason for incorrect discrimination. Therefore, in this paper, we propose a discriminative algorithm utilizing wavelet filter bank to extract unique features between earthquakes and quarry blasts. The discriminative features are found to be in the first five seconds after the onset time. The proposed algorithm is divided into two stages: first, wavelet filter bank extracts the features of the seismic signals; then, support vector machine classifies the event based on these extracted features. The proposed algorithm achieves a discrimination accuracy of 98.5% when applied to 900 earthquakes and quarry blast waveforms.

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Discrimination of earthquakes and explosions using multi-fractal singularity spectrums properties

Cited by 17 publications

References 12 publications

Multifractal analysis for seismic wave in Kathmandu valley after Gorkha Earthquake-2015, Nepal

Multifractal analysis for seismic wave in Kathmandu valley after Gorkha Earthquake-2015, Nepal

Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective

Automatic discrimination of earthquakes and quarry blasts using wavelet filter bank and support vector machine

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