Deep learning can predict tsunami impacts in less than a second riken_en NatureComms
described in the Method section. With this technique, we can efficiently generate tsunami waveforms at S-net stations from many earthquake slip scenarios before simulating the inundation. Therefore, this step is independent of the actual simulation of tsunami scenarios used for the training and testing sets. The analysis aims at estimating the degree of variability of inputs concerning the number of samples for various earthquake magnitudes based on a coefficient of variation.
. Adding more samples beyond the estimated convergence state will no longer contribute to the variability of input, thus is redundant.Coefficient of variation of maximum tsunami amplitudes within 20 min at S-net stations for all considered earthquake magnitudes. The red dashed line indicates the requisite number of samples at each magnitudeRanges of maximum tsunami amplitude used as inputs in the training and test sets in conjunction with inputs for three real tsunami events.
According to the above analysis and a visual inspection, the requisite number of scenarios for the smallest and largest considered megathrust earthquake magnitudes are 310 and 200, respectively. We then apply linear interpolation to the remaining magnitudes illustrated by the red dashed line in Fig., resulting in 3060 scenarios. This implies that about 3000 plate interface earthquake scenarios are needed for the training set.
The prediction is normally updated in real-time applications according to the data availability. Here we perform multiple predictions using maximum and mean tsunami amplitudes within three windows of 10, 15, and 20 min after the earthquake discarding the first 3 min of waveforms to circumvent seismic noise interference elaborated in the Method section. Examples of the type of inputs are illustrated in Supplementary Fig.
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