Zhu Xing, Chen Hui, Wu Zhanglei, Yang Shumei, Li Xiaopeng, Li Tiantao
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China.
College of Computers and Cyber Security, Chengdu University of Technology, Chengdu 610059, China.
Sensors (Basel). 2024 Jul 30;24(15):4947. doi: 10.3390/s24154947.
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.
三段式滑坡以其巨大的规模、隐蔽的发育过程和毁灭性的影响而闻名。本研究进行了物理模型试验，以模拟一种特殊的地质结构，即滑坡体内三段式结构。利用视频图像、微震（MS）信号和声发射（AE）信号，对试验样本的破坏过程和前兆特征进行了细致分析，重点关注事件活动、强度和频率。提出了一种基于AE波形特征的新型分类方法，将AE信号分为突发信号和连续信号。研究结果揭示了这些信号演化过程中的明显差异。突发信号仅出现在裂纹扩展和破坏阶段。在这些阶段，突发信号的累计AE计数逐渐增加，幅度先上升后下降。高幅度突发信号主要分布在中高频段。相比之下，连续信号的累计AE计数迅速上升，幅度单调增加，高幅度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，可作为预警指标。值得注意 的是，AE信号的预警点比视频图像和MS信号的预警点检测得更早。此外，突发信号的预警点比连续信号的预警点出现得更早，且该分类方法的预警点先于整体AE信号的预警点。
