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.

Realtime Monitoring system near Joshimath Uttarakhand, India

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]

Seismic detection of the 7 February 2021 Dhauli Ganga disaster Chamoli District, Uttarakhand, India towards developing an Early Warning System

• 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.

Status of seismic monitoring of Geo Hazards @ CSIR-NGRI