# Realtime Tsunami Prediction System Using Ocean Floor Network for Local Regions
## Real-Time Tsunami Prediction Technique
### Processing for Input Data  
To ensure the accuracy of the predictions, we adopted the logic and steps described in Section 2.4; however, the quality of the real-time data was also one of the issues to be considered. Offsets and noise with frequencies similar to those of a tsunami may cause errors in the predictions. Therefore, it is necessary to identify as many kinds of noise as possible and reduce their effects in real time.  
The signals to be reduced include tides, strong earthquake motions, and changes in the position of the pressure gauge. In addition, data missing because of troubles in data transmission and noise spikes caused by resets of the ocean floor network system are possible. The system considers a certain percentage of missing data to be not unusual but does not use the data if the percentage is higher. The S-net sometimes includes conspicuous noise with a frequency similar to that of a tsunami. The cause may be rotation and pitching of the seafloor observatory caused by strong underwater movement.  
To reduce the effect of high-frequency signals like strong motion recorded in the pressure data, we apply a moving average technique twice using a multi-time window [25]. We currently set the STA, LTA, and VLTA window widths to 50 s, 300 s, and 1 hour, respectively. After removing the tidal signal using the ocean tidal model [26], our system calculates three types of moving averages real time. To record that a tsunami has been triggered, we use the ratio of STA to LTA [9]. This technique can reduce the effects of background noise. In addition, we introduce a temporal gap between the STA and LTA to trigger the first arrival of a tsunami as precisely as possible. The effects on problems caused by missing data and spikes of noise are automatically removed by this system.  
It is important to recognize background noise. In particular, a shallow region of approximately a few hundred meters includes much noise, and the frequency of the noise at some observatories is lower than background noise observed at deeper sites (Figure 8). The effect of triggering by such noise can be restricted by using the ratio of the STA to the LTA, although  
Figure 7: Schematic figure of the tsunami convergence using STA, LTA, VLTA (one time), and VLTA (two times).  
such noise cannot be completely suppressed. We investigated background noise in advance, and we set the threshold level and the window width for calculating the moving average so that the noise issues were satisfactorily mitigated. The temporal gaps between the trigger times and the first arrivals of tsunami waves achieved by using different threshold levels at the different ocean floor observatories were correlated after triggering.  
Noise with a frequency similar to that of a tsunami caused by rotation and pitching of in-line observatories like the S-net cannot be removed at the moment of triggering. In this system, a tsunami is considered to have been triggered if the value of a pressure gauge is a few times larger than the values of gauges at the other observatories around it. The 2021 earthquake off Fukushima-ken (13 March 2021, 23:08 JST; M7.3) did not cause a tsunami, but pressure gauges of 11 observatories (S2N01, S2N02, S2N03, N2S04, S2N10, S2N11, S2N12, S2N13, S2N14, S2N15, and S2N16) were triggered. Such noise was apparent at observatories less than ~100 km from the epicenter. However, there were no signals like a tsunami at the S-net observatories we installed off Chiba Prefecture. In such a case, the tsunami trigger can disappear after a short time because the system judges no tsunami propagation towards Chiba Prefecture.