With the widespread deployment of global seismic networks, the amount of available seismic data has increased substantially, enhancing opportunities to investigate Earth’s dynamics. However, issues such as station clock errors and polarity reversals, which arise from timing system failures, deployment mistakes, or digitization errors, undermine the reliability of seismological research. Rapid and accurate detection and correction of these errors in terabyte-scale datasets are thus essential for effective data preprocessing.

Clock errors are primarily caused by asynchrony between internal and external station clocks. Existing detection methods include comparing P-wave arrival times at nearby stations, and using Green’s functions from ambient noise. The former requires high seismicity and clear phase records, and the latter is limited to stations within small-to-medium apertures due to signal attenuation. Neither method is suitable for global, long-term datasets spanning decades. Consequently, new approaches are needed for efficient and accurate detection of clock errors on a global scale. Polarity reversals, caused by incorrect instrument orientation, cable misconnection, or digitization symbol errors, also require reliable identification and correction to ensure data quality. Although the origins of clock errors and polarity reversals are different, both manifest as phase anomalies in waveform data, affecting measured travel times. Therefore, developing a unified method to detect both errors simultaneously is feasible and necessary.

The method in this paper utilizes the combination of seismic surface wave relative travel-time measurements and P-wave arrivals to efficiently detect both clock error and polarity reversal at stations. Firstly, globally observable long-period surface waves excited by events with M_W≥5.5 are selected. The relative travel-times are obtained by measuring recordings of the same seismic event at different stations. The anomalies of the relative travel-time measurements at a station over a certain time period can reflect the clock errors or polarity reversals of the station. In order to further reduce the possibility of misjudgment in detection using surface wave relative travel-time measurements and to distinguish the clock error and polarity reversal, the proposed method combines the prediction of P-wave arrivals as a secondary check for the anomalous data.

The proposed method was applied to detect long-period surface waveform data from 2 010 stations of 30 networks during 2008−2012. The results show that twelve stations have clock error greater than 10 s in fourteen periods, and seven stations have polarity reversal in eight periods. Clock error detection technique of seismic wave data based on surface wave or P-wave techniques has their advantages and disadvantages. Surface waves have stronger energy and usually propagate over long distances, which can satisfy the detection of stations on a global scale. Due to the existence of interference factors during the measurement of surface waves, it is difficult to ensure absolute accuracy of the results. Although the detection accuracy of P-wave method is higher than that of the surface wave method, the lower signal-to-noise ratio of P-wave propagation over long distances could not satisfy the requirements of the method for a clear P-wave phase. The proposed method combines the two techniques to achieve better detection results.

The proposed method utilizes the cluster analysis for the efficient measurement of the relative travel-times of surface waves and it contains two checks. The first check by surface waves realizes the accurate and fast detection of a large number of data, and the secondary check by the P-wave arrival time ensures the accuracy of the final detection results. The application of the actual waveform data proves that the method can quickly and accurately detect the clock error and polarity reversal of the stations, which improves the quality of global surface wave velocity tomography and also provides a reference for the research on the inversion of source mechanism using relevant data. However, the proposed method based on seismic surface wave technology has its own limitations. Due to measurement errors and source parameter errors, the method can only detect large clock error. Additionally, the method is applicable to the clock error over long time and cannot effectively detect the anomalies of a single event or clock error of less than one day, which need to be further verified by combining with other data. Considering the limitations of the current method, further improvements may be worth considering in the future.