Passive seismic monitoring (PSM) is emerging as a tool for detecting rockfall events and pre-failure seismicity. In this paper, the potential of PSM for rockfall monitoring is assessed through a case study carried out in Gros-Morne, Eastern Qu & eacute;bec, in a region with prominent roadside cliffs, where more than 500 fallen rocks are found on the main regional road each year. The proposed method relies on using sensitive STA-LTA windows to detect a very large number of seismic events and build a comprehensive catalog. In total, more than 70,000 seismic events were detected over one year. Gaussian mixtures are used to partition the data set. Based on visual inspection of the data, a main working hypothesis is that the seismic events can be clustered into three groups. After analyzing the spatio-temporal distribution of the events in each group, we find that the events of one cluster can be associated with anthropogenic activity. The frequency of occurrence of the events of the different clusters and their link with meteorological data is also examined through a regression exercise, to assess the importance of the meteorological variables as explanatory variables. The results allow us to postulate on the physical origins of the signals in the different clusters, attributing them to rockfall activity and wind-induced seismic noise. This study explores the use of passive seismic monitoring (PSM) as a method to detect and track rockfall events, focusing on an area in Eastern Canada, where more than 500 rockfalls reach the main regional road each year. We utilized an automated seismic event detection algorithm to identify over 70,000 seismic events in a one-year period. By analyzing these events with machine learning, we discovered patterns that allowed us to group the events into three categories. One of these groups is linked to human activity, while the other two are influenced by natural factors such as rainfall, strong temperature changes, and wind speed. We interpret the origins of the signals in the latter two groups as rock impacts during rockfalls for one, and strong winds hitting the rock cliff for the other. Our research shows that PSM, coupled with machine learning, is a powerful technique for understanding geomorphological dynamics at a small timescale. We use feature engineering to automatically analyze seismic signals measured on a cliff with frequent rockfalls Clustering proves useful to distinguish signals with different waveforms and frequency content Analysis of the relationships between meteorological variables with each cluster enables their physical interpretation, from geomorphological dynamics to anthropic activity
