The short-term tsunami forecast

A brief overview of the methods of a tsunami early warning in the Kuril Islands, which turned out to be ineffective during recent events, is presented. A hydrophysical method for short-term tsunami forecasting based on information about a tsunami in the ocean, used in the United States, and an express method, also using information about a tsunami in the ocean, are briefly described. The results of the retrospective forecast of the tsunami that occured on March 11, 2011, by the express method are presented.

A Stereo Matching Method for 3D Image Measurement of Long-Distance Sea Surface

Tsunamis are some of the most destructive natural disasters. Some proposed tsunami measurement and arrival prediction systems use a limited number of instruments, then judge the occurrence of the tsunami, forecast its arrival time, location and scale. Since there are a limited number of measurement instruments, there is a possibility that large prediction errors will occur. In order to solve this problem, a long-distance tsunami measurement system based on the binocular stereo vision principle is proposed in this paper. The measuring range is 4–20 km away from the system deployment site. In this paper, we will focus on describing the stereo matching method for the proposed system. This paper proposes a two-step matching method. It first performs fast sparse matching, and then complete high precision dense matching based on the results of the sparse matching. A matching descriptor based on the physical features of sea waves is proposed to solve the matching difficulty caused by the similarity of sea surface image textures. The relationship between disparity and the y coordinate is built to reduce the matching search range. Experiments were conducted on sea surface images with different shooting times and distances; the results verify the effectiveness of the presented method.

Analysis of the tsunami amplification effect by resonance in Yeongil Bay

Predicting tsunami hazards based on the tsunami source, propagation, runup patterns is critical to protect humans and property. Potential tsunami zone, as well as the historical tsunamis in 1983 and 1993, can be a threat to the east coast of South Korea. The Korea Meteorological Administration established a tsunami forecast warning system to reduce damage from tsunamis, but it does not consider tsunami amplification in the bay due to resonance. In this study, the Numerical model, Cornell Multi-grid Coupled Tsunami model, was used to investigate natural frequency in the bay due to coastal geometry. The study area is Yeongill bay in Pohang, southeast of South Korea, because this area is a natural bay and includes three harbors where resonance significantly occurs. This study generated a Gaussian-shaped tsunami, propagated it into the Yeongill bay, and compared numerical modeling results with data from tide gauge located in Yeongill bay during several storms through spectral analysis. It was found that both energies of tsunamis and storms were amplified at the same frequencies, and maximum tsunami wave height was amplified about 3.12 times. The results in this study can contribute to quantifying the amplification of tsunami heights in the bay.

NEAR-FIELD TSUNAMI FORECASTING BASED ON A TSUNAMI RUN-UP RESPONSE FUNCTION

Since near-field-generated tsunamis can arrive within a few minutes to coastal communities and cause immense damage to life and property, tsunami forecasting systems should provide not only accurate but also rapid tsunami run-up estimates. For this reason, most of the tsunami forecasting systems rely on pre-computed databases, which can forecast tsunamis rapidly by selecting the most closely matched scenario from the databases. However, earthquakes not included in the database can occur, and the resulting error in the tsunami forecast may be large for these earthquakes. In this study, we present a new method that can forecast near-field tsunami run-up estimates for any combination of earthquake fault parameters on a real topography in near real-time, hereafter called the Tsunami Run-up Response Function (TRRF).Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/tw1D29dDxmY

Improvement of near-field tsunami forecasting method using ocean-bottom pressure sensor network (S-net)

Since the installation of a dense cabled observation network around the Japan Trench (S-net) by the Japanese government that includes 150 sensors, several tsunami forecasting methods that use the data collected from the ocean floor sensors were developed. One of such methods is the tsunami forecasting method which assimilates the data without any information of earthquakes. The tsunami forecasting method based on the assimilation of the ocean-bottom pressure data near the source area was developed by Tanioka in 2018. However, the method is too simple to be used for an actual station distribution of S-net. To overcome its limitation, we developed an interpolation method to generate the appropriate data at the equally spaced positions for the assimilation from the data observed at sensors in S-net. The method was numerically tested for two large underthrust fault models, a giant earthquake (Mw8.8) and the Nemuro-oki earthquake (Mw8.0) models. Those fault models off Hokkaido in Japan are expected to be ruptured in the future. The weighted interpolation method, in which weights of data are inversely proportional to the square of the distance, showed good results for the tsunami forecast method with the data assimilation. Furthermore, results indicated that the method is applicable to the actual observed data at the S-net stations. The only limitation of the weighted interpolation method is that the computed tsunami wavelengths tend to be longer than the actual tsunamis wavelength.

Improvement of Near-field Tsunami Forecasting Method Using Ocean Bottom Pressure Sensor Network (S-net)

Since the installation of a dense cabled observation network around the Japan Trench (S-net) by the Japanese government that includes 150 sensors, several tsunami forecasting methods that use the data collected from the ocean floor sensors were developed. One of such methods is the tsunami forecasting method which assimilates the data without any information of earthquakes. The tsunami forecasting method based on the assimilation of the ocean-bottom pressure data near the source area was developed by Tanioka in 2018. However, the method is too simple to be used for an actual station distribution of S-net. To overcome its limitation, we developed an interpolation method to generate the appropriate data at the equally spaced positions for the assimilation from the data observed at sensors in S-net. The method was numerically tested for two large underthrust fault models, a giant earthquake (Mw8.8) and the Nemuro-oki earthquake (Mw8.0) models. Those fault models off Hokkaido in Japan are expected to be ruptured in the future. The weighted interpolation method, in which weights of data are inversely proportional to the square of the distance, showed good results for the tsunami forecast method with the data assimilation. Furthermore, results indicated that the method is applicable to the actual observed data at the S-net stations. The only limitation of the weighted interpolation method is that the computed tsunami wavelengths tend to be longer than the actual tsunamis wavelength.

Improvement of Near-field Tsunami Forecasting Method Using Ocean Bottom Pressure Sensor Network (S-net)

Since the installation of a dense cabled observation network around the Japan Trench (S-net) by the Japanese government that includes 150 sensors, several tsunami forecasting methods that use the data collected from the ocean floor sensors were developed. One of such methods is the tsunami forecasting method which assimilates the data without any information of earthquakes. The tsunami forecasting method based on the assimilation of the ocean-bottom pressure data near the source area was developed by Tanioka in 2018. However, the method is too simple to be used for an actual station distribution of S-net. To overcome its limitation, we developed an interpolation method to generate the appropriate data at the equally spaced positions for the assimilation from the data observed at sensors in S-net. The method was numerically tested for two large underthrust fault models, a giant earthquake (Mw8.8) and the Nemuro-oki earthquake (Mw8.0) models. Those fault models off Hokkaido in Japan are expected to be ruptured in the future. The weighted interpolation method, in which weights of data are inversely proportional to the square of the distance, showed good results for the tsunami forecast method with the data assimilation. Furthermore, results indicated that the method is applicable to the actual observed data at the S-net stations. The only limitation of the weighted interpolation method is that the computed tsunami wavelengths tend to be longer than the actual tsunamis wavelength.

Improvement of Near-field Tsunami Forecasting Method Using Ocean Bottom Pressure Sensor Network (S-net)

Since the installation of a dense cabled observation network around the Japan Trench (S-net) by the Japanese government that includs 150 sensors, several tsunami forecasting methods that use the data collected from the ocean floor sensors were developed. One of such methods is the tsunami forecasting method which assimilates the data without any information of earthquakes. The tsunami forecasting method based on the assimilation of the ocean-bottom pressure data near the source area was developed by Tanioka in 2018. However, the method is too simple to be used for an actual station distribution of S-net. To overcome its limitation, we developed an interpolation method to generate the appropriate data at the equally spaced positions for the assimilation from the data observed at sensors in S-net. The method was numerically tested for two large underthrust fault models, a giant earthquake (Mw8.8) and the Nemuro-oki earthquake (Mw8.0) models. Those fault models off Hokkaido in Japan are expected to be ruptured in the future. The weighted interpolation method, in which weights of data are inversely proportional to the square of the distance, showed good results for the tsunami forecast method with the data assimilation. Furthermore, results indicated that the method is applicable to the actual observed data at the S-net stations. The only limitation of the weighted interpolation method is that the computed tsunami wavelengths tend to be longer than the actual tsunamis wavelength.

Sea surface network optimization for tsunami forecasting in the near field: application to the 2015 Illapel earthquake

SUMMARY We propose a method for defining the optimal locations of a network of tsunameters in view of near real-time tsunami forecasting using sea surface data assimilation in the near and middle fields, just outside of the source region. The method requires first the application of the empirical orthogonal function analysis to identify the potential initial locations, followed by an optimization heuristic that minimizes a cost-benefit function to narrow down the number of stations. We apply the method to a synthetic case of the 2015 Mw8.4 Illapel Chile earthquake and show that it is possible to obtain an accurate tsunami forecast for wave heights at near coastal points, not too close to the source, from assimilating data from three tsunameters during 14 min, but with a minimum average time lag of nearly 5 min between simulated and forecasted waveforms. Additional tests show that the time lag is reduced for tsunami sources that are located just outside of the area covered by the tsunameter network. The latter suggests that sea surface data assimilation from a sparse network of stations could be a strong complement for the fastest tsunami early warning systems based on pre-modelled seismic scenarios.