ICAME 2021, Balıkesir, Turkey, 1 - 03 September 2021, no.9, pp.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 , 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.