Ambient seismic noise and microseismicity analyses are increasingly applied for the monitoring of landslides and natural hazards. These methodologies can offer a valuable monitoring tool also for glacial and periglacial bodies, to understand the internal processes driven by external modifications in air temperature and rainfall/snowfall regimes and to forecast possible melting-related hazards in the light of climate change adaptation. We applied the methods to an almost continuous year of data recorded by a network of four passive seismic stations deployed in the frontal portion of the Gran Sometta rock glacier (Aosta Valley, NW Italian Alps). The spectral analysis of ambient seismic noise revealed frequency peaks related to stratigraphic resonances inside the rock glacier. Although the resonance frequency related to the bedrock interface was constant over time, a second higher resonance frequency was identified as the effect of variations in the active layer thickness driven by external air temperature modifications at the daily and seasonal scales. Ambient seismic noise cross-correlation highlighted coherent shear wave velocity modifications inside the periglacial body. The microseismicity dataset extracted from the continuous ambient noise recordings was analyzed and clustered to further investigate the ongoing internal processes and gain insight into their source mechanism and location. The first cluster of events was found to be likely related to the basal movements of the rock glacier and to falls and slides of the debris material. The second cluster was possibly related to shallow ice and rock fracturing processes. The validation of the seismic results through simple models of the rock glacier physical and mechanical layering, the internal thermal regime and the surface displacements allowed for a comprehensive understanding of the rock glacier's reaction to the external conditions.

The Tibetan Plateau, a critical region influencing both local and global atmospheric circulation, climate dynamics, hydrology and terrestrial ecosystems, is undergoing climate-driven changes, including glacial retreat, permafrost thaw and groundwater changes. Despite its importance, implementing continuous and systematic observations has been challenging due to the area's high altitude and extreme climate conditions. In this context, seismic interferometry emerges as a cost-effective method for the continuous monitoring of subsurface structural changes driven by environmental factors and internal geophysical processes. We investigate subsurface evolution using four years of seismic data from nine stations on the northeastern Tibetan Plateau, by applying coda wave interferometry across multiple frequency bands. Our findings highlight seismic velocity changes within the frequency bands 5-10, 0.77-1.54, and 0.25-0.51 Hz, revealing depth-dependent seasonal and long-term changes. Near-surface and deeper strata exhibit similar seasonal patterns, with velocities increasing in winter and decreasing in summer driven by changes in hydrological processes, while intermediate ice-water phase strata show contrasting behaviour due to thermal elastic strain. Long-term trends suggest that the upper subsurface layer is affected by melting water and precipitation originating from Kunlun Mountains, whereas deeper layer reflect groundwater level variations influenced by climate change and human activities. This study provides insights into the environmental evolution of the Tibetan Plateau and its impact on managing local groundwater resources.

Widespread permafrost thaw in response to changing climate conditions has the potential to dramatically impact ecosystems, infrastructure, and the global carbon budget. Ambient seismic noise techniques allow passive subsurface monitoring that could provide new insights into permafrost vulnerability and active-layer processes. Using nearly 2 years of continuous seismic data recorded near Fairbanks, Alaska, we measured relative velocity variations that showed a clear seasonal cycle reflecting active-layer freeze and thaw. Relative to January 2014, velocities increased up to 3% through late spring, decreased to -8% by late August, and then gradually returned to the initial values by the following winter. Velocities responded rapidly (over similar to 2 to 7 days) to discrete hydrologic events and temperature forcing and indicated that spring snowmelt and infiltration events from summer rainfall were particularly influential in propagating thaw across the site. Velocity increases during the fall zero-curtain captured the refreezing process and incremental ice formation. Looking across multiple frequency bands (3-30 Hz), negative relative velocities began at higher frequencies earlier in the summer and then shifted lower when active-layer thaw deepened, suggesting a potential relationship between frequency and thaw depth; however, this response was dependent on interstation distance. Bayesian tomography returned 2-D time-lapse images identifying zones of greatest velocity reduction concentrated in the western side of the array, providing insight into the spatial variability of thaw progression, soil moisture, and drainage. This study demonstrates the potential of passive sei(s)mic monitoring as a new tool for studying site-scale active-layer and permafrost thaw processes at high temporal and spatial resolution.

Cross correlations of seismic noise can potentially record large changes in subsurface velocity due to permafrost dynamics and be valuable for long-term Arctic monitoring. We applied seismic interferometry, using moving window cross-spectral analysis (MWCS), to 2years of ambient noise data recorded in central Alaska to investigate whether seismic noise could be used to quantify relative velocity changes due to seasonal active-layer dynamics. The large velocity changes (>75%) between frozen and thawed soil caused prevalent cycle-skipping which made the method unusable in this setting. We developed an improved MWCS procedure which uses a moving reference to measure daily velocity variations that are then accumulated to recover the full seasonal change. This approach reduced cycle-skipping and recovered a seasonal trend that corresponded well with the timing of active-layer freeze and thaw. This improvement opens the possibility of measuring large velocity changes by using MWCS and permafrost monitoring by using ambient noise.