Automatic seismic wave arrival detection and picking with stationary analysis: Application of the KM2O-Langevin equations

Sho Nakamula,Yasunori Okabe,Minoru Takeo,Masaya Matsuura

doi:10.1186/bf03352719

Abstract

AbstractAn automatic detection and a precise picking of the arrival times of seismic waves using digital seismograms are important for earthquake early detection systems. Here we suggest a new method for detecting and pickingP-andS-wave signals automatically. Compared to methods currently in use, our method requires fewer assumption with properties of the data time series. We divide a record into intervals of equal lengths and check the “local and weak stationarity” of each interval using the theory of the KM2O-Langevin equations. The intervals are stationary when these include only background noise, but the stationarity breaks abruptly when a seismic signal arrives and the intervals include both the background noise and theP-wave. This break of stationarity makes us possible to detectP-wave arrival. We expand the method for picking ofS-waves. We applied our method to earthquake data from Hi-net Japan, and 90% ofP-wave auto-picks were found to be within 0.1 s of the corresponding manual picks, and 70% ofS-wave picks were within 0.1 s of the manual picks. This means that our method is accurate enough to use as a part of the seismic early detection system.

Highlights

We have recently processed a large amount of real time seismic data
We have tested the Test(S) picker using the velocity waveform data set of events selected from National Research Institute for Earth Science and Disaster Prevention’s high-sensitivity seismograph network (Hi-net) Japan data from February, 2002 to July, 2003
We tested the P-wave picker on 334 seismograms and the S-wave picker on 117 seismograms

Summary

Introduction

We have recently processed a large amount of real time seismic data. Many algorithms have been suggested for the automatic picking of seismic signals. Methods using the autoregressive model are based on the assumption that seismograms can be divided into two locally stationary intervals at the time of an arrival of seismic signal, with each interval satisfying a different autoregressive process. AR models are fitted to the time series before and after the dividing point which is assumed to be the arrival of seismic signals, and the value of Copyright c The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences; TERRAPUB

Methods

Results

Conclusion