从连续地震记录快速识别大型滑坡事件的智能方法

An intelligent method for rapid identification of large landslide events from continuous seismic recordings

大型滑坡灾害发生时会产生不同于构造地震活动的震动信号,快速准确识别此类地震信号能为大范围区域滑坡灾害速报或预警提供重要参考.然而,快速准确地从连续地震记录中识别这类信号,是一项具有较大挑战性的工作.本文在全球范围内收集了150个滑坡产生的1431条地震信号,采用基础数据增强方法将数据扩展至含8351条滑坡相关地震信号的数据集,并基于ResNet网络训练了一种用于滑坡事件自动识别的深度学习网络(Landslide recognition network,LRNet),对网络进行了改进,加入了跳接结构和连接结构,用于扩展网络的宽度和特征深度,进一步增强了LRNet的泛化能力.利用已标定的滑坡相关地震数据,对比分析了LRNet、AlexNet、VGGNet和ResNet网络,结果显示LRNet网络具有更高的识别准确率,达到98.14%.通过对西藏林芝地区色东普滑坡灾害连续地震记录的判识,进一步验证了LRNet网络对于滑坡相关地震信号的准确识别率,这表明LRNet具有可靠的泛化性能.文中也利用格林函数反演了主事件的运动轨迹,能够为滑坡灾害的速报及救援提供可靠的技术支持.

Large landslide disasters generate seismic signals distinct from tectonic earthquake activity,and the rapid and accurate identification of such seismic signals from continuous seismic recordings can provide crucial support for rapid reporting or early warning of large-scale landslide disasters.However,achieving such identification poses a significant challenge.In this paper,we collected 1431 seismic signals generated by 150 landslides worldwide.Using basic data augmentation method,we augmented the dataset to 8351 seismic signals related to landslides.We trained a deep learning network,named the landslide recognition network(LRNet),based on the ResNet architecture.The LRNet was enhanced with skip and connection structures to expand network width and feature depth,thereby further enhancing its generalization capability.Using labelled seismic data associated with landslides,LRNet,AlexNet,VGGNet,and ResNet networks were compared and analyzed,with LRNet achieving a recognition accuracy of 98.14%.The LRNet's accurate identification of seismic signals related to landslides was further validated through continuous seismic recordings of the Sedongpu landslide disaster in Linzhi,Tibet,demonstrating reliable generalization performance.Additionally,Green's function inversion was employed to reconstruct the motion trajectory of the main event,providing reliable technical support for rapid reporting and rescue efforts of landslide disasters.