Real-Time Automatic Detectors of P and S Waves Using Singular Value Decomposition

Abstract

We implement a new method for automatic detection of P and S phases using singular value decomposition (SVD) analysis. The method is based on the realtime iteration algorithm of Rosenberger (2010) for the SVD of three-component seismograms. The algorithm identifies the apparent incidence angle by applying SVD and separates the waveforms into their P and S components. We apply the algorithm to filtered waveforms and then either set detectors on the incidence angle and singular values or apply signal-to-noise ratio (SNR) detectors for P and S picking on the filtered and SVD-separated channels. The Anza Seismic Network and the recent portable deployment in the San Jacinto fault zone area provide a very dense seismic network for testing the detection algorithm in a diverse setting, including events with different source mechanisms, stations with different site characteristics, and ray paths that diverge from the approximation used in the SVD algorithm. A 2-30 Hz Butterworth band-pass filter gives the best performance for a large variety of events and stations. We use the SVD detectors on many events and present results from the complex and intense aftershock sequence of the M w 5.2 June 2005 event. This sequence was thoroughly reviewed by several analysts, identifying 294 events in the first hour, all located in a dense cluster around the mainshock. We used this dataset to fine-tune the automatic SVD detection, association, and location, achieving a 37% automatic identification and location of events. All detected events fall within the dense cluster, and there are no false events. An ordinary SNR detector does not exceed 11% success and has a wider spread of locations (not within the reviewed cluster). The preknowledge of the phases picked (P or S) by the SVD detectors significantly reduces the noise created by phase-blind SNR detectors.

Microseismic monitoring using a fibre-optic Distributed Acoustic Sensor (DAS) array

“…Alternatively, other statistical measures such as the Akaike Information Criteria (e.g., Sleeman and van Eck, 1999), signal polarity (e.g., Kurzon et al, 2014), or kurtosis (e.g., Tselentis et al, 2012) are commonly used.…”

Real‐Time Moment Magnitude and Stress Drop with Implications for Real‐Time Shaking Prediction

“…1. Run a P-and S-phase picker (e.g., Kurzon et al, 2014;Ross and Ben-Zion, 2014) and proceed to the next step after S picking.…”

“…E Q -T A R G E T ; t e m p : i n t r a l i n k -; d f 2 ; 3 1 3 ; 2 9 4 Ωf Ω 0 1 f f 0 2 2 (Aki, 1967;Brune, 1970), in which Ω 0 is the low-frequency spectral plateau, which is related to the seismic moment and the hypocentral distance as 3 in which rp is the radiation pattern, FS is a free-surface correction factor, C is the body-wave velocity at the source (P or S, depending on the spectra), R is the hypocentral distance, and ρ is the density. Thus, the hypocentral distance is needed for stress-drop determination, and thanks to recent advances in real-time P-and S-phase picking (e.g., Küperkoch et al, 2012;Kurzon et al, 2014;Ross and Ben-Zion, 2014), its determination in real time is quite robust. Use of equation (2) is appropriate only in cases where the permanent ground displacement is much smaller than the amplitude of the transient motion.…”

Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

“…The latter three used higher order statistics moments in their methods. Other approaches were polarisation filters [14], Singular Value Decomposition [15], wavelet [16], [17], [18], neural networks [19], [20] and autoregressive models [21]. Some methods combined existed approaches such as in [22].…”