In the wake of the Japan disaster, scientists aim to issue faster and more accurate tsunami warnings

MANCHESTER – In the ten years following the Great East Japan Earthquake and Tsunami of 11 March 2011, scientists have sought answers to a variety of questions related to the deadly tsunamis that began tearing coastal communities just 15 minutes after the earthquake.

Researchers have investigated how a tsunami collects altitude as it approaches the shore and how this affects the damage it could cause. They have also begun to evaluate early tsunami detection techniques and improve tsunami monitoring systems across Japan.

“The speed of a tsunami outside is the same as that of a jet plane and its internal velocity is the same as that of Usain Bolt,” says Nobuhito Mori, a professor in the Department of Coastal Disaster Research at the Institute for Disaster Prevention Research at Kyoto University.

While tsunamis are fast, they are not as fast-moving as earthquakes themselves. It slows down and produces higher waves in shallow water.

Moreover, the behavior of tsunamis becomes less understood once they begin to travel through streets and buildings, making it difficult to make accurate predictions.

“That’s why early evacuation is so important,” says Morey.

The JMA issued a local tsunami warning three minutes after the earthquake, and the Pacific Tsunami Warning Center, operated by the US National Oceanic Atmospheric Administration (NOAA), issued its regional warning six minutes later. Response times were an indication of the complex and heavy-duty calculations required for tsunami warnings. The NOAA first had to determine if the earthquake had occurred in the ocean basin, the likely state of ocean floor deformation, and the type of motion that caused it.

The JMA warning has been criticized for reducing the size of the tsunami. The agency acknowledged that underestimated expectations led to slow evacuations, giving some people just 15 minutes to evacuate in the most affected areas. In March 2013, the agency updated its warning system and introduced new analysis procedures based on the maximum possible earthquake.

A tsunami is a series of extremely long waves created by a sudden large displacement of the ocean, usually the result of an earthquake below or near the ocean floor. This force creates outward waves in all directions away from its source, and at times crosses entire ocean basins. Whereas wind-driven waves only travel through the highest layer of the ocean, tsunamis move across the entire column of water, from the ocean floor to the surface.

The World Historical Tsunami Database indicates that since 1900, more than 80% of potential tsunamis have been generated by earthquakes at destructive plate boundaries. However, tsunamis can also occur due to landslides and volcanic activity. Once a tsunami forms, its velocity depends on the depth and wavelength of the ocean, and the distance from the summit to the summit can be hundreds of kilometers.

Morey says a tsunami is more difficult to detect than earthquakes early and that alerts take longer to generate

“The current tsunami prediction by the JMA is based on selecting the closest tsunami scenario from a large number of pre-computed tsunami scenario databases that focus on epicenter and fault information. This makes tsunami prediction a waste of time because the source information is needed first. “

Scientists are looking at ways to speed up computer alerts without sacrificing accuracy.

One of the latest projects, announced in February, is looking at how a new AI model could harness the power of the world’s fastest supercomputer, Fugaku, to predict floods in coastal areas before a tsunami makes landfall.

The new technology was developed by a collaborative team of researchers from the International Research Institute for Disaster Science (IREDeS) at Tohoku University, the Seismological Research Institute at the University of Tokyo and Fujitsu Laboratories.

Scientists used Fugaku to generate training data for 20,000 potential tsunami scenarios based on high-resolution simulations. The AI ​​model uses marine waveform data generated from a tsunami scenario to predict floods before landing.

While traditional prediction techniques require the use of supercomputers, once Fugaku has been trained, the AI ​​model can be run on regular computers. The research team applied the model to simulate a tsunami flood in Tokyo Bay on a standard computer. Predictions proved highly accurate in a matter of seconds, matching flood modeling data released by the Cabinet Office.

The research can make it possible to obtain more accurate and rapid flood forecasts in specific areas, as well as predict the potential impact of local waves on buildings and roads in coastal urban areas. Researchers will present new scenarios and continue to work with the system this year with the goal of being able to use artificial intelligence to predict tsunami floods in wider areas. It is hoped that the system will also help make evacuation procedures more efficient.

The potential of AI demonstrates the advancement of technology since the first form of tsunami prevention, with coastal Japanese communities cutting warnings into stones. Hundreds of these tsunami stones, some of which date back over six centuries, dot the country’s coast, standing in silent testimony of the devastation that has occurred in the past. Recently, the hardest hit areas have been showing signs of rising water levels in 2011.

The tsunami warning system in the country has been strengthened in recent years through various additional systems, which allow the JMA to more accurately estimate tsunami heights and wave arrival times. The warning system collects data from tsunami sensors on the ocean floor offshore, GPS buoys, tsunami gauges in the coastal area, strong motion broadband seismometers and DART buoys.

Morey says the installation of a large-scale observatory network on the sea floor to detect tsunamis and earthquakes, called S-net, in 2017 has proven effective so far.

The S-net system consists of 150 seabed observatories, connected by submarine optical cables, with a total length of about 5,700 km along the Corel Trench and Japan. Each observatory has multiple seismometers and highly sensitive water depth sensors for tsunami detection.

Morey believes that GPS buoys and tsunami sensors at the ocean floor are currently the most accurate way to spot tsunamis. However, he believes there is room for development and is of the view that evaluating technologies, such as AI models, have a potential impact in the long term.

It concludes that “despite the importance of an early warning and more reliable system for evacuation, a long-term assessment combining hardware and software countermeasures will save many lives.”

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