{"title":"基于利用合成孔径雷达相干时间序列的递归神经网络的大规模建筑物损坏评估:2023 年土耳其-叙利亚地震案例研究","authors":"Yanchen Yang, Chou Xie, Bangsen Tian, Yihong Guo, Yu Zhu, Ying Yang, Haoran Fang, Shuaichen Bian, Ming Zhang","doi":"10.1177/87552930241262761","DOIUrl":null,"url":null,"abstract":"The Turkey–Syria earthquakes that occurred on February 6, 2023, have caused significant human casualties and economic damage. Emergency services require quick and accurate assessments of widespread building damage in affected areas. This can be facilitated by using remote sensing methods, specifically all-day and all-weather Synthetic Aperture Radar (SAR). In this study, we aimed to improve the detection of building anomalies in earthquake-affected areas using SAR images. To achieve this, we employed Recurrent Neural Network (RNN) to train coherence time series and predict co-seismic coherence. This approach allowed us to generate a Damage Proxy Map (DPM) for building damage assessment. The results of our study indicated that the estimated proportion of building damage in Kahramanmaras was approximately 24.08%. These findings were consistent with the actual damage observed in the field. Moreover, when utilizing the mean and standard deviation of coherence time series, our method achieved higher accuracy (0.761) and a lower false alarm rate (0.136) compared to directly using coherence with only two views of SAR data. Overall, our study demonstrates that this method provides an accurate and reliable approach for post-earthquake building damage assessment.","PeriodicalId":11392,"journal":{"name":"Earthquake Spectra","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large-scale building damage assessment based on recurrent neural networks using SAR coherence time series: A case study of 2023 Turkey–Syria earthquake\",\"authors\":\"Yanchen Yang, Chou Xie, Bangsen Tian, Yihong Guo, Yu Zhu, Ying Yang, Haoran Fang, Shuaichen Bian, Ming Zhang\",\"doi\":\"10.1177/87552930241262761\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Turkey–Syria earthquakes that occurred on February 6, 2023, have caused significant human casualties and economic damage. Emergency services require quick and accurate assessments of widespread building damage in affected areas. This can be facilitated by using remote sensing methods, specifically all-day and all-weather Synthetic Aperture Radar (SAR). In this study, we aimed to improve the detection of building anomalies in earthquake-affected areas using SAR images. To achieve this, we employed Recurrent Neural Network (RNN) to train coherence time series and predict co-seismic coherence. This approach allowed us to generate a Damage Proxy Map (DPM) for building damage assessment. The results of our study indicated that the estimated proportion of building damage in Kahramanmaras was approximately 24.08%. These findings were consistent with the actual damage observed in the field. Moreover, when utilizing the mean and standard deviation of coherence time series, our method achieved higher accuracy (0.761) and a lower false alarm rate (0.136) compared to directly using coherence with only two views of SAR data. Overall, our study demonstrates that this method provides an accurate and reliable approach for post-earthquake building damage assessment.\",\"PeriodicalId\":11392,\"journal\":{\"name\":\"Earthquake Spectra\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earthquake Spectra\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/87552930241262761\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earthquake Spectra","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/87552930241262761","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Large-scale building damage assessment based on recurrent neural networks using SAR coherence time series: A case study of 2023 Turkey–Syria earthquake
The Turkey–Syria earthquakes that occurred on February 6, 2023, have caused significant human casualties and economic damage. Emergency services require quick and accurate assessments of widespread building damage in affected areas. This can be facilitated by using remote sensing methods, specifically all-day and all-weather Synthetic Aperture Radar (SAR). In this study, we aimed to improve the detection of building anomalies in earthquake-affected areas using SAR images. To achieve this, we employed Recurrent Neural Network (RNN) to train coherence time series and predict co-seismic coherence. This approach allowed us to generate a Damage Proxy Map (DPM) for building damage assessment. The results of our study indicated that the estimated proportion of building damage in Kahramanmaras was approximately 24.08%. These findings were consistent with the actual damage observed in the field. Moreover, when utilizing the mean and standard deviation of coherence time series, our method achieved higher accuracy (0.761) and a lower false alarm rate (0.136) compared to directly using coherence with only two views of SAR data. Overall, our study demonstrates that this method provides an accurate and reliable approach for post-earthquake building damage assessment.
期刊介绍:
Earthquake Spectra, the professional peer-reviewed journal of the Earthquake Engineering Research Institute (EERI), serves as the publication of record for the development of earthquake engineering practice, earthquake codes and regulations, earthquake public policy, and earthquake investigation reports. The journal is published quarterly in both printed and online editions in February, May, August, and November, with additional special edition issues.
EERI established Earthquake Spectra with the purpose of improving the practice of earthquake hazards mitigation, preparedness, and recovery — serving the informational needs of the diverse professionals engaged in earthquake risk reduction: civil, geotechnical, mechanical, and structural engineers; geologists, seismologists, and other earth scientists; architects and city planners; public officials; social scientists; and researchers.