Articles | Volume 16, issue 12
Research article
09 Dec 2016
Research article |  | 09 Dec 2016

Tsunami arrival time detection system applicable to discontinuous time series data with outliers

Jun-Whan Lee, Sun-Cheon Park, Duk Kee Lee, and Jong Ho Lee

Abstract. Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after the recording is restarted. In order to avoid such false alarms and time delays, we propose the Tsunami Arrival time Detection System (TADS), which can be applied to discontinuous time series data with outliers. TADS consists of three algorithms, outlier removal, gap filling, and tsunami detection, which are designed to update whenever new data are acquired. After calibrating the thresholds and parameters for the Ulleung-do surge gauge located in the East Sea (Sea of Japan), Korea, the performance of TADS was discussed based on a 1-year dataset with historical tsunamis and synthetic tsunamis. The results show that the overall performance of TADS is effective in detecting a tsunami signal superimposed on both outliers and gaps.

Short summary
Water level sensors often experience unexpected gaps and outliers that cause major difficulties in detecting tsunamis. Thus, we propose a tsunami arrival time detection system applicable to discontinuous time-series data with outliers. We want to stress that the efficiency and simplicity of the system enable its wide application in tsunami monitoring areas.
Final-revised paper