A novel earthquake onset detection method using wavelet packet transform

Speaker: Kanchan Aggarwal

Venue : HSB 210

Date : 24th May, 2019

Time : 5.15 p.m.

Abstract

Detecting the onset of P-waves in seismic signals is a crucial objective in the development of early warning systems for earthquake-prone regions. We present a novel time-frequency based method to efficiently detect and pick the onset of P-wave in seismic signals with low SNR (signal-to-noise ratio). The proposed technique rests on a combination of time-series modelling of seismic noise and wavelet packet transform (WPT), in which the core idea involves tracking the difference between energies of data and one-step ahead model predictions over a select set of wavelet packets (frequency bands). Auto-regressive integrated moving average (ARIMA) models are used for modelling seismic noise, while the packets are selected using the prevailing understanding of P-wave frequency content. The proposed method is superior to the existing methods in two respects, (i) accuracy of detection and picking since it zooms into the frequency bands of interest (corresponding to P-wave onset) and (ii) robustness in the sense of minimal false alarms due to outliers and other sources, especially from low SNR seismograms.

About the speaker

Kanchan Aggarwal is a Ph.D. scholar in the Department of Chemical Engineering at IIT Madras. She is working on Seismic data analysis under Prof. Arun K Tangirala in a project supported by Bhabha Atomic Research Centre (BARC) India. Prior to this, she graduated with a B.Tech Degree (Electronics and Instrumentation Engineering) from Uttar Pradesh Technical University, 2014.