Data Quality Analysis of China Permanent Seismic Network by Repeating Earthquakes

Highly similar waveforms recorded from repeating earthquakes can be utilized to evaluate the data quality of a seismic station. We used a hypothesis testing method to establish a data quality detection model based on repeating earthquakes. The model effectiveness was verified by using waveforms of a pair of repeating earthquakes, which occurred in northeastern Japan on 20 March 2021 (Mw = 7.0) and 1 May 2021 (Mw = 6.9), from 109 stations in the Global Seismographic Network. A total of 842 permanent broadband stations in mainland China were evaluated using this model. Eighteen anomalies were found mainly attributed to calibration, instrument noise, mass recentering, and regional long-period interference. We found that most of the stations function well. Moreover, utilizing repeating earthquakes to analyze the waveform quality can circumvent the need for extensive forward calculations, as well as greatly reduce the influence of source parameter uncertainties and structural complexity on the seismogram. Additionally, the need for detection in other datasets in different regional networks has broadened the scope of these applications.

Automated summary

Highly similar waveforms recorded from repeating earthquakes can be utilized to evaluate the data quality of seismic stations. We established a data quality detection model based on repeating earthquakes and verified its effectiveness by evaluating 842 permanent broadband stations in mainland China. Most of the stations function well, and utilizing repeating earthquakes to analyze waveform quality can reduce the need for extensive calculations and minimize the impact of uncertainties and structural complexity on the results.