An automated protocol for filter processing of strong-motion records: Case study in China

A new earthquake early warning network with over 15,000 stations in China generates a large volume of strong-motion records annually, making manual processing inefficient and subjective. This study presents an automated method for selecting the high-pass filter cut-off frequency (f_HP) for Chinese strong-motion records, based on manually processed data from 2008 to 2020. The study highlights the importance of considering the time-domain responses of causal and acausal filters when assessing filtering results and introduces rational constraints to optimize f_HP selection across different magnitudes and epicentral distances. While effective for most records, manual selection remains necessary for cases with significant surface waves. To evaluate its performance, the method was compared with the method proposed by Ramos-Sepulveda et al. and applied to datasets from China, CESMD (U.S.), K-NET and KiK-net (Japan), and ESM (Europe). Results show that the proposed method improves the consistency of f_HP selection and retains more seismic signal information compared to existing methods. However, regional variations in noise characteristics and manual selection criteria influence the outcomes, highlighting the need for careful application. By enhancing the efficiency and reliability of strong-motion data processing, this study supports more accurate seismic analyses and earthquake hazard assessments.