Identification of strong motion record baseline drift based on Bayesian-optimized Transformer network

Research in earthquake engineering heavily relies on strong motion observation. The quality of strong motion records directly affects the reliability of earthquake disaster prevention, rapid reporting of seismic magnitude, earthquake early warning, and other areas. Currently, basic mathematical methods, such as zero-line adjustment and filtering, are commonly employed to ensure the quality of strong motion records. However, these methods often rely on subjective judgment based on human experience when dealing with abnormal waveforms in strong motion records, leading to relatively low efficiency. To address this challenge, this paper proposes an innovative Transformer model based on Bayesian optimization to efficiently identify baseline drift anomalies in strong motion records. By partitioning the strong motion record data from the 1999 Chi-Chi earthquake in Taiwan, China, into two categories: high-quality records (with minimal baseline drift) and low-quality records (with significant baseline drift), we extracted data with distinct features and inputted them into the proposed model for training. Data with distinct features were extracted and input into the proposed model for training. Finally, the model was used to predict whether strong motion records exhibited baseline drift abnormalities. The experimental results show that the optimized Transformer model achieves a performance exceeding 85% in key evaluation metrics such as accuracy and F1 scores. It is capable of efficiently identifying a substantial volume of strong motion records with baseline drift within a short period of time. The model effectively performs the baseline drift classification task for strong motion records and can be used for subsequent identification of abnormalities after baseline drift correction, enabling automation in handling abnormal data related to baseline drift.