Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection

• Published in: Geofluids Vol. 2019; no. 2019; pp. 1 - 14
• Main Authors: Mu, Dawei, Chen, Po, Lin, Guan-Wei, Liao, Wu-Yu, Lee, En-Jui, Lin, Ching-Weei
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; Hindawi Limited; Hindawi-Wiley
• Subjects: Automation; Broadband; Constraint modelling; Detection; Earthquakes; Fatalities; Forecasting; Hazards; Image detection; Landslides; Landslides & mudslides; Learning algorithms; Locating; Machine learning; Mathematical models; Medical imaging; Modelling; Monitoring systems; Physics; Rain; Rainfall; Rainfall forecasting; S waves; Satellite imagery; Satellites; Science; Seismic activity; Seismic waves; Soil sciences; Storm damage; Topography; Training; Typhoons; Pacific Ocean; Taiwan
• ISSN: 1468-8115; 1468-8123
• DOI: 10.1155/2019/1426019

Rainfall-triggered landslides are one of the most deadly natural hazards in many regions. Seismic recordings have been used to examine source mechanisms and to develop monitoring systems of landslides. We present a semiautomatic algorithm for detecting and locating landslide events using both broadband and short-period recordings and have successfully applied our system to landslides in Taiwan. Compared to local earthquake recordings, the recordings of landslides usually show longer durations and lack distinctive P and S wave arrivals; therefore, the back projection method is adopted for the landslide detection and location. To identify the potential landslide events, the seismic recordings are band-passed from 1 to 3 Hz and the spectrum pattern in the time-frequency domain is used to distinguish landslides from other types of seismic sources based upon carefully selected empirical criteria. Satellite images before and after the detected and located landslide events are used for final confirmation. Our landslide detection and spatial-temporal location system could potentially benefit the establishment of rainfall-triggered landslide forecast models and provide more reliable constraints for physics-based landslide modeling. The accumulating seismic recordings of landslide events could be used as a training dataset for machine learning techniques, which will allow us to fully automate our system in the near future.