Landslide-generated seismic waves (landslide-quakes), exhibiting distinctive waveforms and frequency
characteristics, can be recorded by nearby seismometers. Implementing an automatic classifier for landslidequakes
could help provide objective and accurate initiation times of landslides with efficiency. This study collected
and analyzed 214 large scale landslide seismic records from the Broadband Array in Taiwan for Seismology (BATS).
In addition, equal numbers of earthquake and noise signals were also incorporated. The 642 seismic signals and time
information were carefully examined to create an automatic landslide-quake classifier. By validating the signal attributes of the landslide, earthquake, and noise events, specifically in the time and frequency domains, it was shown that the proposed classifier can reach an accuracy of 91.3 %. To further evaluate the applicability of the automatic classifier,landslide-quakes generated during the devastating Typhoon Morakot (2009) and Typhoon Soudelor (2015) were also verified, showing that the sensitivity of the classifier is higher than 98 %.
Key Words: Large scale landslide, Seismic signal, Machine learning, Signal attributers