Infrasound signal characteristics of rock soil landslide and experience with its engineering application

As the infrasound signal has the advantages of slow energy attenuation, strong ability to cross obstacles and no need of contact acquisition, it is of great significance to take advantage of the infrasound signal in the process of deformation and failure of rock and soil mass to realize remote rapid monitoring and early warning of geological disasters. The infrasound signal characteristics of soil slope failure and rock under different stress states (compression, shear and tension) were compared by indoor and outdoor tests. The results showed that there was an obvious waveform of infrasound signal at the site of soil slope damage. The infrasound signal appeared mainly in elastic and plastic deformation stages under the compression state, the peak frequency of the infrasound signal was about 7 Hz. The concentration of signal power was slightly less than that under the compression state, and the peak frequency was about 8 Hz. The infrasound signal always associated with the whole loading process under tension state, and there were two bands of frequency center, in which the lower frequency was close to that of the compression test specimen, and the higher frequency was 3 Hz larger. On this basis, using the infrasound characteristics of rock and soil mass failure, the infrasound and other monitoring methods were carried out for Xinpu landslide in Fengjie, Chongqing, China. After the occurrence of the landslide, the infrasound signal characteristics of rock and soil failure were basically the same as those of indoor tests. The low-frequency signals were mainly monitored. At the same time, the monitoring results showed that the peak value of the infrasound signal reached before the mechanical signal, and the mechanical signal was monitored prior to the displacement signal. The infrasound signal can be 3-5 hours ahead of displacement signal. This regularity has important scientific and application value for landslide monitoring and prediction.