DING L S,XIE J B,WU H D,LIAO Y F,YE S S,LU Z J,MA J M,LV Z. 2024. ANALYSIS OF MONITORING CAPABILITY OF STRONG MOTION SATION IN EASTERN GUANGDONG BASED ON THE PROBABILITY INTERVAL OF AMBIENT NOISE. Acta Seismologica Sinica,46(3):1−17. doi: 10.11939/jass.20220146
Citation: DING L S,XIE J B,WU H D,LIAO Y F,YE S S,LU Z J,MA J M,LV Z. 2024. ANALYSIS OF MONITORING CAPABILITY OF STRONG MOTION SATION IN EASTERN GUANGDONG BASED ON THE PROBABILITY INTERVAL OF AMBIENT NOISE. Acta Seismologica Sinica,46(3):1−17. doi: 10.11939/jass.20220146

ANALYSIS OF MONITORING CAPABILITY OF STRONG MOTION SATION IN EASTERN GUANGDONG BASED ON THE PROBABILITY INTERVAL OF AMBIENT NOISE

  • Observation exploits the spectral statistical characteristics of strong motion ambient noise through acceleration RMS density function, for estimating the quality of records from and status of the real-time strong motion station. Since then, the monitoring capability analysis method of the strong seismic station has been outlined, which is the acceleration RMS average model, the minimum mode and the probability interval of ambient noise of a strong-motion station are compared with the frequency-amplitude lines, which estimated from the regional earthquake events with epicenteral distance of 70~150 km from each station and magnitudes range from M7.0 down to M2.0. This procedure not only shows the dynamic performance of the strong motion station, but also demonstrates the monitoring quality directly. Through this method, we get : The monitoring capability analysis method can be used to estimate the probability of the different magnitude regional seismic events recorded by strong motion station, so as to evaluate the monitoring capability of station. Affected by the environmental noise of the station, each station can clearly observe different earthquake magnitudes from the others. And the different lower limits of noise recorded by different stations is due to the interaction of instrument self-noise of accelerometers and environmental noise. The acceleration RMS minimum model of strong motion station can be used as the estimation of optimal monitoring capability of the station and is a comprehensive index of self noise of the accelerometer and noise of the observation environment. The data from the Pearl River Delta Early Warning Network in Guangdong, China.
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