The spike recognition in strong motion records model based on improved feature extraction method and SVM

Abstract

Spike is a type of abnormal waveform observed in strong motion records. It is characterized by a sudden and significant increase in peak acceleration, exceeding the theoretical range, and being greatly uncoordinated with the surrounding record points. The spike severely affects the reliability of subsequent analysis of strong motion records. This paper conducts research based on the strong motion records of the Turkey-Syria earthquakes in 2023. It focuses on the spike represented as Peak Ground Acceleration (PGA) and proposes a spike recognition model for strong motion records using Bayesian-optimized Support Vector Machine (SVM). Firstly, a preprocessing method for strong motion records based on adaptive waveform scaling is proposed. This method enables the adaptive normalization of strong motion records with different scales, thereby eliminating the influence of different scales on the accuracy of manual annotations. Furthermore, a new method for characterizing strong motion records is proposed. It captures shape features (two-dimensional) through one-dimensional data, enabling the spatial distribution of strong motion records to be represented by a shape feature vector. Finally, a Bayesian optimization-based improved SVM model is proposed. It utilizes the feature extraction method proposed in this paper for the identification of spikes in strong motion records. The experimental results show that the proposed model in this paper achieved excellent performance, with an F1-score of 0.868 and an AUC value of 0.93 on the test set.