Exploring the Possibility of Detecting Tsunami Waves using Partial Data

Abstract

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A tsunami is a series of water waves caused by many factors like earthquakes, volcanic eruptions, landslides and meteorite impacts. This study involves the identification of tsunami waves using partial data before it reaches the shoreline that can be applied as a forecasting tool. The results of the study were based 01'1 a discrete-event simulation of a prototype tsunami wave in Deep Ocean and real data of a tsunami event in 2003. Fast Fourier Transform (FFT) was the mathematical tool, which was used in the study. Truncation error occurred when a limited portion of the prototype wave was observed. The exponential function tapered window played a major role in reducing this truncation error. The results showed that when 25% of the prototype tsunami wave started to emerge, it is possible to identify that the wave was of tsunami type and when 37% of the wave starts to emerge it can be verified that the wave is a tsunami wave. The validity of this result was checked by real data of a tsunami event occurred on 17 November 2003. It showed that when nearly 25% of the wave was generated, it could be identified that the wave was of tsunami type. Finally, a tsunami wave superposed with a ship wave in Deep Ocean showed that they have similar patterns and it was not difficult to interpret that the superposed wave was tsunami. The results obtained from this study can be used to forecast whether a tsunami wave is generated. Henceforth, the responsible authorities can take necessary actions to evacuate the residents to safe areas and to reduce the number of false alerts.