Real-Time Event Detection, Phase Identification and Source Location Estimation Using Single Station Three-Component Seismic Data

Roberts, Roland; Christoffersson, Anders; Cassidy, Frances

doi:10.1111/j.1365-246x.1989.tb00517.x

Cited by 119 publications

(81 citation statements)

References 6 publications

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“…This approach is very useful in areas with sparse seismic networks [3,4]. Automatic computation algorithms in a single broadband three-component station have been mainly developed for P and S waves onsets detection, allowing the estimation of source location using the back-azimuth and the apparent surface speed measurements [5][6][7], or seismic moment estimation [8][9][10][11][12][13]. On the other hand, kernel-based methods have become a very powerful tool for mathematicians, scientists and engineers, providing a very rich and surprising solution in areas such as signal processing and pattern recognition [14].…”

Section: Introductionmentioning

confidence: 99%

Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques

Gutiérrez

Vargas

Vasquez

2018

DYNA

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A Support Vector Machine Regression (SVMR) algorithm was applied to calculate the epicenter distance using a ten seconds signal, after primary waves arrive at a seismological station near to Bogota -Colombia. This algorithm was tested with 863 records of earthquakes, where the input parameters were an exponential function of waveform envelope estimated by least squares and maximum value of recorded waveforms for each component of the seismic station. Cross validation was applied to normalized polynomial kernel functions, obtaining mean absolute error for different exponents and complexity parameters. The epicenter distance was estimated with 10.3 kilometers of absolute error, improving the results previously obtained for this hypocentral parameter. The proposed algorithm is easy to implement in hardware and can be employed directly in the field, generating fast decisions at seismological control centers increasing the possibilities of effective reactions.Keywords: earthquake early warning; support vector machine regression; earthquake; rapid response; epicenter distance; seismic event; seismology; Bogota -Colombia.Estimación rápida de la distancia epicentral de un terremoto utilizando registros de una sola estación sismológica, mediante técnicas de aprendizaje de máquinas Resumen Se aplicó un algoritmo de máquinas de vector de soporte para calcular la distancia epicentral utilizando una señal de diez segundos, después del arribo de ondas primarias a una estación sismológica cercana a Bogotá -Colombia. Este algoritmo fue probado con 863 registros de terremotos donde los parámetros de entrada fueron una función exponencial de la envolvente estimada para los mínimos cuadrados y el valor máximo de las formas de ondas registradas en cada componente de la estación sísmica. Validación cruzada fue aplicada a funciones kernel polinomiales normalizadas, obteniendo la media del error absoluto para diferentes exponentes y parámetros de complejidad. La distancia epicentral se estimó con 10.3 kilómetros de error absoluto, mejorando los resultados previamente obtenidos para este parámetro hipocentral. El algoritmo propuesto es fácil de implementar y puede ser empleado directamente en campo, generando decisiones rápidas en centros de control sismológico incrementado posibilidades de tener reacciones efectivas.Palabras clave: alerta temprana de terremotos; máquinas de soporte vectorial; terremoto; respuesta rápida; distancia epicentral; evento sísmico; sismología; Bogotá -Colombia.

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Section: Introductionmentioning

confidence: 99%

Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques

Gutiérrez

Vargas

Vasquez

2018

DYNA

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“…Over the last two decades, numerous algorithms have been developed for P arrival identification based on energy analysis [1]- [5], polarization analysis [6]- [10], artificial neural networks [11], [12], maximum likelihood methods [13], [14], fuzzy logic theory [15], autoregressive techniques [16]- [19], higher-order statistics [20]- [24], sample of a sequence.…”

Section: Introductionmentioning

confidence: 99%

Seismic $P$ Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain

Galiana-Merino

Rosa-Herranz

Parolai

2008

IEEE Trans. Geosci. Remote Sensing

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The P seismic phase first arrival identification is a fundamental problem in seismology. The accurate identification of the P-wave first arrival is not a trivial process, especially when the seismograms present a very low signal-to noise ratio (SNR) or are contaminated with artificial transients that could produce false alarms. In this paper, a new approach based on higher-order statistics and the stationary wavelet transform is presented. The P onset is obtained under a statistical criterion applied in the time-frequency domain. The results have been compared to those estimated by other P phase picking algorithm and P onsets picked by expert analysts. The comparison shows that our proposed method efficiently provides a good estimate of the P onset picks that are consistent with analyst picks, especially in the cases of very low SNR.Index Terms -Kurtosis, P phase identification, seismic signal processing, stationary wavelet transform (SWT).3

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“…Accurately determining the time of these arrivals is important in determining the location of an evenl such as an earthquake or explosion. Traditionally this has been done using data from various locations, however recently research has been carried out in trying to determine this information more accurately from just a single station seismogram (Kanasewich, 1981;Roberts et al, 1989;Magotra et al, 1989;Jarpe and Dowla, 1991), especially for small regional events. In this work, I extend the polarization analysis methods ofKanasewich(198 1) and others (Park, 1987;Means, 1972;Fowler et al, 1967), to the wavelet coefficients instead of the original signal.…”

Section: Introductionmentioning

confidence: 99%

Wavelet transform analysis of transient signals: the seismogram and the electrocardiogram

Anant¹

1997

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Real-Time Event Detection, Phase Identification and Source Location Estimation Using Single Station Three-Component Seismic Data

Cited by 119 publications

References 6 publications

Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques

Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques

Seismic $P$ Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain

Wavelet transform analysis of transient signals: the seismogram and the electrocardiogram

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