Adaptive analysis on in-situ accelerometer calibration in strong-motion observation network in China

Abstract

The sampling filters used in the National Strong-Motion Observation Network System (NSMONS) of China are linear-phase filters for both real-time transmitted data and locally stored raw data. The performance evaluation of accelerometer is based on human visual assessment through their step calibration response. However, a new re-sampling requirement has been added for the real-time transmitted data of the Earthquake Early Warning System (EEWS) in China, which has a large number of stations. This involves utilizing minimum-phase filters. There are significant challenges in the performance evaluation of accelerometers and the verification of parameter consistency for different data sets within the EEWS in China. Therefore, we propose a method that integrates waveform adaptive identification and analysis of the dynamic characteristics of the accelerometer's step response in the time domain to calculate the overshoot ratio and its changes over time. Our findings indicate that the overshoot ratios of accelerometers, decimated from linear-phase filters and minimum-phase filters, are approximately 7%–9% and 21%–23% respectively under normal sensor operation. Additionally, we reveal the effects of humidity and instrument aging on changes in the transform function of a Force-Balanced Accelerometer (FBA). In practical applications at NSMONS and EEWS in China, the proposed method can automatically identify the starting point of step calibration and detect parameter setting errors and faulty stations. This method has significant generalization potential.