Abstract
The discrete time wavelet transform has been used to develop software that detects seismic P and S-phases. The detection algorithm is based on the enhanced amplitude and polarization information provided by the wavelet transform coefficients of the raw seismic data. The algorithm detects phases, determines arrival times and indicates the seismic event direction from three component seismic data that represents the ground displacement in three orthogonal directions. The essential concept is that strong features of the seismic signal are present in the wavelet coefficients across several scales of time and direction. The P-phase is detected by generating a function using polarization information while S-phase is detected by generating a function based on the transverse to radial amplitude ratio. These functions are shown to be very effective metrics in detecting P and S-phases and for determining their arrival times for low signal-to-noise arrivals. Results are compared with arrival times obtained by a human analyst as well as with a standard STA/LTA algorithm from local and regional earthquakes and found to be consistent.
Highlights
Seismic events such as earthquakes cause a release of energy represented by seismic waves that can be recorded by seismic monitoring stations
Automatic detection techniques are of interest because they can be processed in near real-time; they apply a consistent set of metrics and are repeatable
The software is developed in Matlab 6.5TM including signal processing and WavelabTM toolboxes. It consists of two algorithms: the P-phase detection algorithm and S-phase detection algorithm described in the sections below
Summary
Seismic events such as earthquakes cause a release of energy represented by seismic waves that can be recorded by seismic monitoring stations. Detection of seismic waves and estimation of their arrival times provides information about earthquake location and magnitude. It is often very difficult to determine consistent estimates of P and S waves if they have low signal-to-noise characteristics, if arriving at seismic stations at regional distances. The wavelet theory arises in 1909 when Haar constructs the first orthonormal system of compactly supported functions called the Haar basis [15]. The wavelet theory has been applied on seismic signals by Grossmann and Morlet in 1984 [11]. The software is developed in Matlab 6.5TM including signal processing and WavelabTM toolboxes. It consists of two algorithms: the P-phase detection algorithm and S-phase detection algorithm described in the sections below. The P-phase detection algorithm can be categorized into three main modules: 1) DTWT Processing that includes the following: Multiresolution Analysis
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