This study is based on the strong motion daily records from the dense network of seismic monitoring and early warning systems of the Pearl River Delta in eastern Guangdong. By utilizing the RMS density function of background noise at strong motion stations, we investigate the statistical characteristics of the background noise spectrum at these stations. We establish the average model, minimum model, and probability distribution interval for the RMS of background noise. This forms the basis for a method to analyze the monitoring capabilities of strong motion stations by comparing the probability distribution intervals of background noise RMS with the frequency-acceleration amplitude distribution curves of regional earthquake events.Using this method, we obtain the daily background noise acceleration RMS for different stations and estimate the probability of recording regional seismic events of various magnitudes, thereby evaluating the monitoring capabilities of the stations. The lower noise limits for different stations vary due to the interaction between instrument self-noise and environmental noise. The minimum model of background noise RMS can be used as an estimate of the optimal monitoring capability of a station, serving as a comprehensive indicator of both the strong motion instrument and the observation environment. This also contributes to discussions on the low-end cut-off frequency for denoising in the frequency domain within engineering seismology. The probability distribution interval analysis method for background noise RMS is an extension and expansion of the probability density distribution analysis of background noise RMS.
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