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Enhancing Real-Time Landslide Detection for Improved Tsunamigenic Landslides in Alaska

In response to the increasing prevalence of mass movements, such as landslides, coupled with the urgent need for early warning systems, this study presents significant advancements in refining real-time landslide detection. Currently, we run a real-time landslide detector based on long-period seismic data within the dynamic landscape of southern Alaska. Over the past year, we have compiled a diverse dataset encompassing landslides (0.2 - 10 M m3), as well as regional (M 3-5) and teleseismic seismic events that triggered false detections.

In this study, we refine detection techniques by combining historical landslides, which benefit from well-studied properties, with a diverse set of landslides detected in real time during the 2023 season. We employ a range of waveform similarity measures, explore different signal processing criteria, and assess various detection thresholds to derive sets of statistics aimed at enhancing detection performance. Our primary objective is to substantially improve accuracy and reliability in the early detection of landslides, with a particular focus on tsunamigenic events. The ultimate goal of this research is to provide a robust and efficient solution for early detection of tsunamigenic landslides, contributing to improved warning systems and mitigation strategies in coastal regions.

Session: Detecting, Characterizing and Monitoring Mass Movements - II

Type: Oral

Room: Kahtnu 2

Date: 5/2/2024

Presentation Time: 11:30 AM (local time)

Presenting Author: Ezgi Karasozen

Student Presenter: No

Additional Authors