This study investigates subsurface weathering zones, revealing their structure through shear wave velocity variations. The research focuses on the arid climate of Pan de Azúcar National Park, Chile, using seismic ambient noise recordings to construct pseudo-3D models. The resulting models show the subsurface structure, including granite gradients and mafic dike intrusions. Comparison with other sites emphasizes the intricate relationship between climate, geology, and weathering depth.

Natural disasters such as landslides and rockfalls are mostly difficult to study because of the impossibility of making in situ measurements due to their destructive nature and spontaneous occurrence. Seismology is able to record the occurrence of such events from a distance and in real time. In this study, we show that, by using a machine learning approach, the mass and velocity of rockfalls can be estimated from the seismic signal they generate.

Floods draining beneath an ice cap are hazardous events that generate six different short- or long-lasting types of seismic signals. We use these signals to see the collapse of the ice once the water has left the lake, the propagation of the flood front to the terminus, hydrothermal explosions and boiling in the bedrock beneath the drained lake, and increased water flow at rapids in the glacial river. We can thus track the flood and assess the associated hazards better in future flooding events.

Landslide processes are causes of major concern to population and infrastructures on Réunion. In this study, we used data from the Copernicus Sentinel-1 satellite to map ground motion in Cirque de Salazie. We concentrate on the cyclonic season 2017–2018. Our results show ground motion in the Hell-Bourg, Ilet à Vidot, Grand-Ilet, Camp Pierrot, and Le Bélier landslides. Moreover, we show an unknown pattern of ground motion situated in a non-instrumented, uninhabited area on the ground.

Microseismic events recorded on the Åknes rock slope in Norway during the past 15 years are automatically divided into eight classes. The results are analysed and compared to meteorological data, showing a strong increase in the microseismic activity in spring mainly due to freezing and thawing processes.