Akademik Veri Yönetim Sistemi
ICAME 2021, Balıkesir, Türkiye, 1 - 03 Eylül 2021, sa.9, ss.1-6
Tsunamis are
among the most devastating hazards that can be observed in nature. Observation,
sensing, recording, and analysis of the tsunami and tsunami-structure interaction
parameters are of crucial importance for the safety of the coastal zone and
communities. These parameters include but are not limited to tsunami water
surface fluctuations, particle velocities, inundation, runup, sediment deposit,
their dynamics pressures on structures. Efficient sensing, data recording, and
analysis of these parameters is critically important for the reconnaissance,
assessment, early warning, and avoidance of catastrophic consequences of
tsunamis. One of the most successful sensing algorithms of the big data era is
the compressive sensing technique (CS), which can outperform classical sampling
methodologies by using far fewer samples while achieving exact recovery [1, 2].
In this paper, we investigate the possible usage of the CS for the effective
measurement and reconstruction of the tsunami parameters of water surface
fluctuation, particle velocities, and tsunami-induced wave pressures. Using the
data sets of the Japanese Tohoku Tsunami occurred in 2011 after a major
earthquake of Mw 9.0 [3, 4], provided by the USA’s National Oceanic
and Atmospheric Administration (NOAA)’s Deep-Ocean Assessment and Reporting of
Tsunamis (DART) portal, we show that CS can be used as an effective tool
for the measurement, analysis, and reconstruction of the tsunami and
tsunami-structure interaction parameters. Although we limit ourselves with the
reconstruction of water surface fluctuations and tsunami-induced dynamic
pressures [5], the CS can be applied for monitoring of the tsunami parameters in
more general settings including the effects of vortices and shorter waves [6, 7].
We discuss our findings and comment on their possible applicability and usage.