Understanding dynamics of ground movement based on seismic monitoring

Given the unique geology, topography, and hydrology of the Himalayas, it is imperative that a long-term strategy be developed to reduce the destructive potential of landslides in the region. Monitoring and an early warning system for landslides are the best non-structural measures for preventing landslide disasters. The study's overarching objective is to highlight why it's crucial to keep an eye out for landslides and how seismic sensor arrays can be used to issue early warnings. The implications of large mass flows in the study area must be carefully considered for sustainable hydropower and other socio-economic development projects. Seismic data, satellite imagery data, Time-Frequency analysis (TFA), and videos and photos taken by eyewitnesses form the basis of our investigation.

First, we gather precise event data, and then we collect signals from the seismology observatory at the Indian Institute of Technology Roorkee for that time frame. The signals from the collection have been processed with signal processing methods like STA/LTA, Filtering, and TFA. One synthetic signal, two landslide events, and two local earthquakes were analyzed to better comprehend the dynamics and behavior of a natural distractive event.

Seismic records reveal that various types of events have distinctive dynamic properties. There are three distinct stages to a landslide event: (1) the detachment of slope-forming materials, (2) the debris flow, and (3) the flood flow. The P and S waves, the onset and end times, and the duration of an earthquake have all been determined. We have used synthetic signals to learn about TFA and have found the method that works best for interpreting seismic signals. We use the classification method developed by Provost et al. in 2017. Time-domain amplitude levels are a feature that can be easily extracted and classified, but they are also vulnerable to noise. Energy concentration in the time-frequency domain is one such method that, while requiring more complex operations, can lead to more trustworthy feature extraction and accurate classification. The absence of the distinct P- and S-wave arrival time, as is typical of earthquakes, is another feature of the seismic waveform that is indicative of a landslide. The results of the seismic record analysis also shed light on the breadth of monitoring for slope-moving disaster events in the North-West Himalayas.