The mountainous region usually witness Natural disasters such as landslides, GLOFs, and flash floods, triggered
by factors like river erosion, extreme weathering, heavy rainfall, permafrost thawing, human activities and
earthquakes. Extreme weather variations heighten slope destabilization risk in high-altitude areas. Glacial
retreat and permafrost melting expand glacial lakes, destabilizing slopes and increasing landslide and flood
risks downstream.
The seismic signals can effectively be utilized to understand environmental processes beyond earthquakes. It
investigates anthropogenic noise from human activities that affect the accuracy of seismic monitoring. Research
related to E.S explores how natural processes like weather patterns, hydrological cycles and land use changes
influence seismic signals. For instance, heavy rainfall increases soil saturation and alters seismic wave
propagation. Melting glaciers induce seismic activity and isostatic rebound. Environmental seismology monitors
landslides, volcanic eruptions, and glacial dynamics, detecting precursory signals for early warning and hazard
mitigation. Technological advancements enable precise seismic data collection and analysis, fostering
interdisciplinary collaboration of experts in geophysics, hydrology, meteorology, and ecology. This
multidisciplinary approach aids in understanding complex Earth processes, natural hazard assessment, resource
management, and environmental conservation efforts. Environmental seismology remains crucial for safeguarding
communities and ecosystems in a changing world.
The environmental seismology group of CSIR-NGRI has expertise in understanding the seismic signals generated
by Natural disasters, like landslides, glacial lake outburst floods (GLOFs) and flash floods. In addition to
the analysis of data from pre-existing networks in the vicinity of the Himalayas, the group is operating two
dedicated landslide monitoring networks in Uttarakhand and Arunachal Pradesh to study the precursory
subsurface changes associated with the landslide processes. The AI/ML approaches have been used for waveform
discrimination and automatic detection of earthquake and landslide signals.
Major Inferences from the Study.
Large flow events can be detected across a seismic network, and can be tracked as they move
downstream.
The NGRI network would have been sufficient for early warning of the 7 Feb event, with upto ~30
minutes warning for Joshimath, 10 minutes for Tapovan, and a few minutes for Rishi Ganga.
Study suggests that early warning based on regional seismic networks is very much possible. [Rao
et al., 2021, Science; Cook et al., 2021, Science]
We initiated the development of seismic signal based Landslide Early Warning (LEW) system for
Uttarakhand.
Designed the machine learning based Automatic Seismic signal classifier in the first phase of
LEW system.
Detected the seismic signals of 15-07-2011 flood and landslides from seismic recordings in
CSIR-NGRI Arunachal Pradesh Network.
The stations at nearly 100 km able to record the landslide signals even in the high background
noise.