| Abstract Submission No. | ABS-3.1-0104 |
| Title of Abstract | Identification of Landslide-Generated Seismic Signals: Case studies from Arunachal Pradesh, northeast India |
| Authors | R P Singh, Himangshu Paul*, B Laxman, Naba K. Borah |
| Organisation | CSIR- National Geophysical Reseach Institute, Hyderabad-500007 |
| Address | Cyber Building, CSIR-NGRI Hyderabad, Telangana, India Pincode: 500008 E-mail: raviprakash47010@gmail.com |
| Country | India |
| Presentation | Poster |
| Abstract | Landslides are identified as one of the world¿s most dangerous natural hazards due to their potential to cause immense threat to life and infrastructure. Spatio-temporal information of mass flow processes like landslides, rockslides, avalanches, etc is of utmost importance for disaster management and mitigation. Prediction of such events are not possible, however, an early-warning may be generated for downslope regions of an imminent threat. The rapid analysis and assessment of the event is critical for generating early warning. However, traditional methods like optical imagery and field investigations are time-consuming and do not provide the necessary temporal resolution to generate warning. Recent studies show that seismological signals from dense seismic networks is a plausible option in this regard. Although, seismic signals provide high temporal resolution, it is not straightforward to identify seismic signals of landslides because of the lack of sharp onset and absence of body-wave arrivals in their records as compared to events like earthquakes. In this study, we attempt to identify seismic signals of a few landslides using a local network of broadband seismological stations. This type of analysis is novel and is being carried out for the first time in Arunachal Pradesh. In 2022, CSIR-NGRI established a network of broadband seismic network in Itanagar with inter-station spacing of ~3 km. We utilized this data to analyze and identify seismic signals of landslides in the vicinity of the network during 2022-2023. By analyzing continuous time-series and spectrogram analysis, the landslides signals could be identified. In general, landslide energy appear as spindle-shaped in the spectrogram, compared to triangular-shaped for earthquakes. The landslides are located by a noise-correlation based source migration method. The analysis shows that the density of seismic stations is very critical for accurate location of the landslides. |
| Keywords | landslides, spectrogram, location |