Abstract. In this study, we present an optimization method for determining a cost-effective sparse configuration for tsunami gauges to realize the reconstruction of high-resolution wave height distribution throughout the target region based on the concept of super-resolution. This optimization method consists of three procedures. First, we generate time series data of tsunami wave heights at synthetic gauges by conducting numerical simulations of various earthquake and tsunami scenarios at the target site. Next, we apply proper orthogonal decomposition to the synthetic tsunami data to extract the spatial features of the wave height distribution. Finally, according to these spatial features, an optimization process is performed to determine a sparse configuration of synthetic gauges. In the optimization, the optimal gauges are sequentially selected from the set of synthetic gauges to reconstruct the wave height distribution with the highest accuracy. Targeting hypothetical Nankai Trough earthquakes and tsunamis, we determine the optimal configuration near Shikoku and demonstrate the wave height reconstruction capability of the approach by comparing the performance of networks with optimally and randomly placed gauges. The results indicate that coastal gauges contribute more to improving the reconstruction accuracy and that a configuration with 21 optimal gauges has satisfactory performance. In addition, we assess the performance of the existing NOWPHAS network installed in the Shikoku region and find that the reconstruction performance of the existing network is equivalent to that of the optimal gauge network.
Reprint (published Open Access): FujitaNomura2024.pdf
bibtex entry:
@article{FujitaNomura2024,
author = {S. Fujita and R. Nomura and S. Moriguchi and
Y. Otake and S. Koshimura and R. J. LeVeque and K. Terada},
title = {Optimization of a Tsunami Gauge Configuration for
Pseudo-Super-Resolution of Wave Height Distribution},
journal = {Earth and Space Science},
volume = {11},
pages = {e2023EA003144},
year = {2024},
doi = {10.1029/2023EA003144},
}