Landslide Monitoring in Joshimath through Passive Seismology and SAR Interferometry

Landslides remain one of the most pervasive natural hazards in the Himalayan region, exacerbated by intense rainfall, steep topography, and anthropogenic activity. Accurate detection, monitoring, and hazard zoning of these slope failures are essential for mitigating their impact on communities and infrastructure. This research integrates Synthetic Aperture Radar Interferometry (InSAR) with Seismic Ambient Noise Interferometry (SANI) monitoring to provide a comprehensive assessment of landslide-prone regions. Using time-series InSAR techniques such as Persistent Scatterer (PS-InSAR) and Small Baseline Subset (SBAS) interferometry, implemented through open-source tools like OpenSAR Lab, MintPy, LiCSBAS, and StaMPS, we processed multi-temporal SAR datasets from January 2020 to December 2024 to retrieve surface deformation rates. This analysis enabled the identification of both active and slowly deforming landslides across the study area. 

The seismic velocity change was computed to monitor subsurface behavior from 17th June 2024 to 17th July 2024 using three seismic stations, viz AULI, CHAD, and KVSJ, located in Joshimath, Chamoli. The dv/v calculate for KVSJ shows a prominent velocity drop on July 5th, 2024. The landslides occurred on the 9th and 10th of July 2024. The drop in velocity can be attributed to increased moisture due to precipitation, which results in rigidity loss. The observed velocity drop may be interpreted as a precursor signal to landslide occurrence. However, the dv/v plots for the AULI and CHAD do not show any prominent drop in the velocity. The one possible reason could be the aspect of the downslope area that orients to a relatively stable valley bottom. As the region hosts metasedimentary rock, the subsurface response to the infiltration of rainwater could also have contributed to the different behavior of these two stations, and this is to be further investigated to identify the plausible causes.

The integrated methodology presented in this work demonstrates that the synergy between InSAR and passive seismology not only improves landslide detection and monitoring capabilities but also contributes to more informed early warning systems and risk reduction strategies. This study contributes to the broader goal of disaster-resilient infrastructure planning in mountainous terrains.