流离失所(心理学)
大地测量学
鉴定(生物学)
地质学
滤波器(信号处理)
平坦度(宇宙学)
计算机科学
地震学
物理
计算机视觉
心理学
宇宙学
植物
量子力学
生物
心理治疗师
作者
Baofeng Zhou,W. -X. Guo,Yefei Ren,Ruizhi Wen,Hongwei Wang,Tong Xu,Cong Zhang,Aiwen Liu
出处
期刊:Earthquake engineering and resilience
日期:2024-03-01
卷期号:3 (1): 120-136
被引量:6
摘要
Abstract On February 6, 2023, the Republic of Turkey experienced a rare occurrence of two successive earthquakes, each with a magnitude exceeding 7.0. The Disaster and Emergency Management Agency (AFAD) swiftly shared the strong motion records, thereby enriching the global database of near‐fault strong motion records. Identification of permanent displacement is essential for effectively utilizing these records. In this study, we refined the permanent displacement identification method, which combines the Hermit interpolation baseline correction with flatness determination by incorporating a low‐pass filter. Following this, we compared four permanent displacement identification methods, including our improved approach. We applied these to the strong motion record of station 4404 and compared the results with the Global Positioning System coseismic displacement. At the same time, we used field investigation data to verify the effectiveness of our improved method, studying its applicability for both single‐wave packet and multiwave packet records. The conclusions are the following: the improved method provides a more reasonable and effective means of identifying permanent displacement. When the peak ground acceleration (PGA) exceeds 1 g, the permanent displacement identifications from the four methods differ significantly. The discrepancy in permanent displacements identified by the four methods in the horizontal direction is larger than that in the U−D direction. For the record with the largest PGA (station 4614), our improved method yields more reasonable results compared with other techniques. Furthermore, the choice of segmentation time nodes in the velocity time history significantly affects the identification of permanent displacement.
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