IoT-Driven Microseismic Sensing System and Monitoring Platform for Landslide Detection

Landslides pose a significant threat to human life and infrastructure, causing extensive damage and fatalities. Effective monitoring and dissemination of early warnings of imminent landslides are constrained by a lack of precise spatial and temporal information on landslide triggers and uncertainties of the factors that lead to such events. This paper addresses these issues by presenting an Internet of Things (IoT)- driven platform designed to capture microseismic vibrations in landslide-prone areas. The proposed system aims to provide insights into the onsets of hazardous landslides, particularly those stimulated by heavy rainfall and earthquakes. This treatise utilizes a microseismic smart sensing system with geophone sensors (SM-s nodes) which continuously records and transmits real-time data on seismic activities associated with potential landslides, enabling timely propagation of early warnings. The proffered system’s ability to acquire and characterize microseismic signals was systematically validated through an integrated set of landslide laboratory experiments, outdoor field trials, and real-world deployment in Chandmari located in the State of Sikkim, India, situated in the North-Eastern Himalayan region. Furthermore, the paper provides an in-depth analysis of the historical microseismic activities, differentiating them from ambient noises such as pedestrian and vehicular movements and slope instabilities triggered by rainfall and earthquakes. The system’s performance was evaluated during three real-world events: two earthquakes and an instance of rainfall precipitation. This study explored the time and frequency characteristics as well as the variations of ground motion parameters during recorded slope instabilities. A comparative analysis of existing microseismic monitoring approaches was also conducted to assess the effectiveness of the proposed system. The insights gained from this work were instrumental for the development of decision models capable of identifying precursory microseismic activities precedent to imminent landslides, towards safeguard of lives and property damage.