Detection of P-wave Onset in Seismic Signals using Wavelet Packet Transform

Aggarwal, Kanchan; Mukhopadhyay, Siddhartha; Tangirala, Arun K.

doi:10.23919/sice.2019.8859808

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…A comparative analysis with STA/LTA, AIC, and WPT-AIC methods is presented to demonstrate the superiority of the proposed method. Preliminary ideas of the specific methodology being taken up were presented at a conference [9], where we demonstrated its applicability without alluding to any generic framework or the estimation of P-wave using the reconstructed signal. Moreover, [9] does not prescribe a method for t-f band selection and excludes any comparative analysis with the existing methods for different datasets with varying SNR scenarios.…”

Section: Introductionmentioning

confidence: 99%

A prediction framework with time-frequency localization feature for detecting the onset of seismic events

2021

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Onset detection of P-wave in seismic signals is of vital importance to seismologists because it is not only crucial to the development of early warning systems but it also aids in estimating the seismic source parameters. All the existing P-wave onset detection methods are based on a combination of statistical signal processing and time-series modeling ideas. However, these methods do not adequately accommodate some advanced ideas that exist in fault detection literature, especially those based on predictive analytics. When combined with a time-frequency (t-f) / temporal-spectral localization method, the effectiveness of such methods is enhanced significantly. This work proposes a novel real-time automatic P-wave detector and picker in the prediction framework with a time-frequency localization feature. The proposed approach brings a diverse set of capabilities in accurately detecting the P-wave onset, especially in low signal-to-noise ratio (SNR) conditions that all the existing methods fail to attain. The core idea is to monitor the difference in squared magnitudes of one-step-ahead predictions and measurements in the time-frequency bands with a statistically determined threshold. The proposed framework essentially accommodates any suitable prediction methodology and time-frequency transformation. We demonstrate the proposed framework by deploying auto-regressive integrated moving average (ARIMA) models for predictions and the well-known maximal overlap discrete wavelet packet transform (MODWPT) for the t-f projection of measurements. The ability and efficacy of the proposed method, especially in detecting P-waves embedded in low SNR measurements, is illustrated on a synthetic data set and 200 real-time data sets spanning four different geographical regions. A comparison with three prominently used detectors, namely, STA/LTA, AIC, and DWT-AIC, shows improved detection rate for low SNR events, better accuracy of detection and picking, decreased false alarm rate, and robustness to outliers in data. Specifically, the proposed method yields a detection rate of 89% and a false alarm rate of 11.11%, which are significantly better than those of existing methods.

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Section: Introductionmentioning

confidence: 99%

A prediction framework with time-frequency localization feature for detecting the onset of seismic events

2021

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Timely detection of Seismic waves in Ground motion data using Improved S-Transform

Sharma

Nanda

2020

2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE)

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A Consistently Processed Strong-Motion Database for Chilean Earthquakes

Castro

Benavente

Crempien

et al. 2022

Seismological Research Letters

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Since the 1985 M 8.0 central Chile earthquake, national strong-motion seismic networks have recorded ten megathrust earthquakes with magnitudes greater than M 7.5 at the convergent margin, defined by the contact between the Nazca and South American plates. The analysis of these earthquake records have led to improved hazard analyses and design codes for conventional and seismically protected structures. Although strong-motion baseline correction is required for a meaningful interpretation of these records, correction methods have not been applied consistently in time. The inconsistencies between correction methods have been neglected in the practical use of these records in practice. Consequently, this work aims to provide a new strong-motion database for researchers and engineers, which has been processed by traceable and consistent data processing techniques. The record database comes from three uncorrected strong motion Chilean databases. All the records are corrected using a four-step novel methodology, which detects the P-wave arrival and introduces a baseline correction based on the reversible-jump Markov chain Monte Carlo method. The resulting strong motion database has more than 2000 events from 1985 to the date, and it is available to download at the Simulation Based Earthquake Risk and Resilience of Interdependent Systems and Networks (SIBER-RISK) project website.

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Detection of P-wave Onset in Seismic Signals using Wavelet Packet Transform

Cited by 3 publications

References 13 publications

A prediction framework with time-frequency localization feature for detecting the onset of seismic events

A prediction framework with time-frequency localization feature for detecting the onset of seismic events

Timely detection of Seismic waves in Ground motion data using Improved S-Transform

A Consistently Processed Strong-Motion Database for Chilean Earthquakes

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