An efficient physic-based event detection algorithm inspired by music information retrieval

In the last decade, the increasing number and spatial density of seismological stations provide unprecedented opportunities for recording various natural and human-related events in continuous records. Diverse methods have been proposed for event detection, classification, and characterization, but few of them are based on the physical properties of the events. In this study, inspired by music information retrieval methods such as audio fingerprinting, we present a time-efficient event detection method based on capturing the physical properties of seismic signatures such as corner frequency, high-frequency fall-off, and complexity of signature. The zero-crossing rate of the recorded signal is used to estimate the corner frequency, which is the dominant frequency in the velocity domain of record. The high-frequency fall-off can be estimated in the time-frequency spectrogram by finding the frequency below which 75% of the energy of the spectrum is produced. The complexity of the spectrum of the recorded signal is finally represented by a second-order polynomial coefficient fitting the spectrum and capturing the slope of the source spectra. Also, we use the spectral flatness to quantify the noise properties. We validate the proposed procedure to synthetic data generated by the stochastic simulation method. We finally apply the method to real data sets to detect the seismic precursors for the Nuugaatsiaq landslide. We separate the earthquake event and precursory signals because of different corner frequencies and show that the precursory signals started for hours before the main landslide.