Identification of Strong Motion Record Baseline Drift Based on Bayesian Optimized Transformer Network

Abstract

Abstract 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, the quality assurance of strong motion records often relies on basic methods such as zero-line adjustment and high-pass filtering. However, these methods often fail to satisfactorily identify and handle abnormal waveforms present in strong motion records, and their efficiency is relatively low. In this paper, a Bayesian-optimized Transformer-based approach is proposed to improve the identification of 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. Finally, the model was used to predict whether strong motion records exhibited baseline drift abnormalities. The experimental results show that after performing Bayesian optimization on the parameters of the Transformer model, this method achieves an accuracy of over 85% in baseline drift identification. It is capable of efficiently identifying a large volume of strong motion records with baseline drift within a short period of time. The model performs well in the task of classifying baseline drift in strong motion records and can be used for subsequent identification of abnormalities after baseline drift correction, enabling automation in handling baseline drift abnormal data.