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Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2024-11-21 , DOI: 10.1111/mice.13377
Zhiyi Tang, Jiaxing Guo, Yinhao Wang, Wei Xu, Yuequan Bao, Jingran He, Youqi Zhang

Structural health monitoring (SHM) aims to assess civil infrastructures' performance and ensure safety. Automated detection of in situ events of interest, such as earthquakes, from extensive continuous monitoring data, is important to ensure the timeliness of subsequent data analysis. To overcome the poor timeliness of manual identification and the inconsistency of sensors, this paper proposes an automated seismic event detection procedure with interpretability and robustness. The sensor‐wise raw time series is transformed into image data, enhancing the separability of classification while endowing with visual understandability. Vision Transformers (ViTs) and Residual Networks (ResNets) aided by a heat map–based visual interpretation technique are used for image classification. Multitype faulty data that could disturb the seismic event detection are considered in the classification. Then, divergent results from multiple sensors are fused by Bayesian fusion, outputting a consistent seismic detection result. A real‐world monitoring data set of four seismic responses of a pair of long‐span bridges is used for method validation. At the classification stage, ResNet 34 achieved the best accuracy of over 90% with minimal training cost. After Bayesian fusion, globally consistent and accurate seismic detection results can be obtained using a ResNet or ViT. The proposed approach effectively localizes seismic events within multisource, multifault monitoring data, achieving automated and consistent seismic event detection.

中文翻译:


使用深度学习和贝叶斯融合自动检测考虑错误数据干扰的地震事件



结构健康监测 (SHM) 旨在评估民用基础设施的性能并确保安全。从大量的连续监测数据中自动检测感兴趣的原位事件,例如地震,对于确保后续数据分析的及时性非常重要。为了克服人工识别时效性差和传感器不一致的问题,本文提出了一种具有可解释性和鲁棒性的自动化地震事件检测程序。传感器的原始时间序列被转换为图像数据,增强了分类的可分离性,同时赋予了视觉可理解性。Vision Transformers (ViT) 和残差网络 (ResNets) 在基于热图的视觉解释技术的帮助下用于图像分类。在分类中考虑了可能干扰地震事件检测的多类型错误数据。然后,通过贝叶斯聚变融合来自多个传感器的不同结果,输出一致的地震探测结果。使用一对大跨度桥梁的四个地震响应的真实监测数据集进行方法验证。在分类阶段,ResNet 34 以最小的训练成本实现了超过 90% 的最佳准确率。贝叶斯聚变后,可以使用 ResNet 或 ViT 获得全球一致且准确的地震探测结果。所提出的方法有效地在多源、多故障监测数据中定位地震事件,实现自动化和一致的地震事件检测。
更新日期:2024-11-21
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