PDF(Pubmed)
Abstract：
Three-section landslides are renowned for their immense size, concealed development process, and devastating impact. This study conducted physical model tests to simulate one special geological structure called a three-section-within landslide. The failure process and precursory characteristics of the tested samples were meticulously analyzed using video imagery, micro-seismic (MS) signals, and acoustic emission (AE) signals, with a focus on event activity, intensity, and frequency. A novel classification method based on AE waveform characteristics was proposed, categorizing AE signals into burst signals and continuous signals. The findings reveal distinct differences in the evolution of these signals. Burst signals appeared exclusively during the crack propagation and failure stages. During these stages, the cumulative AE hits of burst signals increased gradually, with amplitude rising and then declining. High-amplitude burst signals were predominantly distributed in the middle- and high-frequency bands. In contrast, cumulative AE hits of continuous signals escalated rapidly, with amplitude monotonously increasing, and high-amplitude continuous signals were primarily distributed in the low-frequency band. The emergence of burst signals and high-frequency AE signals indicated the generation of microcracks, serving as early-warning indicators. Notably, the early-warning points of AE signals were detected earlier than those of video imagery and MS signals. Furthermore, the early-warning point of burst signals occurred earlier than those of continuous signals, and the early-warning point of the classification method preceded that of overall AE signals.
摘要：
三段滑坡以其巨大的规模而闻名，隐蔽的开发过程，和毁灭性的影响。这项研究进行了物理模型试验，以模拟一种称为三段内滑坡的特殊地质结构。使用视频图像对测试样品的失效过程和前兆特征进行了细致的分析，微震（MS）信号，和声发射(AE)信号，专注于事件活动，强度,和频率。提出了一种新的基于AE波形特征的分类方法,将AE信号分为突发信号和连续信号。这些发现揭示了这些信号演变的明显差异。突发信号仅在裂纹扩展和失效阶段出现。在这些阶段,突发信号的累积AE命中逐渐增加，振幅上升然后下降。高振幅突发信号主要分布在中高频带。相比之下,连续信号的累积AE命中迅速升级，随着振幅单调增加，高振幅连续信号主要分布在低频段。突发信号和高频AE信号的出现表明微裂纹的产生，作为预警指标。值得注意的是,AE信号的预警点比视频图像和MS信号的预警点更早。此外，突发信号的预警点比连续信号的预警点发生得更早，分类方法的预警点先于总体AE信号。
