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

• Autorzy: Zhou Baofeng , Yin Yue , Wang Maofa , Zhang Runjie , Zhang Yue , Guo Wenheng

Identyfikatory

DOI

10.1007/s11600-024-01460-x

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

transformer network; Bayesian optimization; sequence classification; baseline drift; strong motion record

PL

sieć transformerowa; optymalizacja bayesowska

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2025
• Tom: Vol. 73, no. 1
• Strony: 517--525
• Opis fizyczny: Bibliogr. 36 poz.

Twórcy

autor

Zhou Baofeng

• Key Laboratory of Earthquake Engineering and Engineering Motion, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
• Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China

autor

Yin Yue

• Key Laboratory of Earthquake Engineering and Engineering Motion, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
• Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China

autor

Wang Maofa

• Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
• School of Information Engineering, Institute of Disaster Prevention, Langfang 065201, China

• https://orcid.org/0000-0001-6517-4042

autor

Zhang Runjie

• Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
• School of Information Engineering, Institute of Disaster Prevention, Langfang 065201, China

autor

Zhang Yue

• Key Laboratory of Earthquake Engineering and Engineering Motion, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
• Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China

autor

Guo Wenheng

• School of Information Engineering, Institute of Disaster Prevention, Langfang 065201, China

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).