Originating from underwater earthquakes or volcanic eruptions, tsunamis are enormous water waves capable of travelling as fast as jet planes over deep waters. They build higher and higher as they move inland, striking brutal blows to lives and livelihoods in coastal areas.
Despite their calamitous potential, tsunamis can be particularly sneaky, necessitating the development of robust early warning systems to predict their arrival. And the current warning systems, which rely on sea buoys and seismic sensors to measure underwater earthquakes, aren’t always accurate in predicting the dangers of resulting tsunamis.
To fix this, Cardiff University-led study has proposed utilising something that can travel much faster than tsunami waves: acoustic-gravity waves.
Acoustic-gravity waves are naturally occurring sound waves that move thousands of kilometres through the deep sea and traverse significantly faster than tsunami waves, carrying crucial information about the earthquake and its originating source.
The new system can quickly classify submarine earthquakes and determine the risk of tsunami events by integrating acoustic technology with artificial intelligence (AI) to monitor tectonic activity in real time.
"Our study demonstrates how to obtain fast and reliable information about the size and scale of tsunamis by monitoring acoustic-gravity waves, which travel through the water much faster than tsunami waves enabling more time for evacuation of locations before landfall," explains Dr Usama Kadri, the study's co-author.
To create this system, researchers used sound recordings (acoustic radiation) captured by underwater microphones (hydrophones) to measure the acoustic radiation produced by 200 earthquakes that jolted the Pacific and Indian Oceans.
Then, the team used a computational model to triangulate the source of the tectonic event using the above hydrophone recordings, along with the data from existing sea buoys and seismic sensors.
And finally, the AI algorithms classified the quake's slip type (the type of fault which causes the earthquake), magnitude, and other vital properties that determine the tsunami wave's size.
Since tsunami risk is highly dependent on the features of the underwater earthquake that triggers it, real-time classification of these earthquakes would enable earlier and more reliable tsunami alerts.
This study was recently published in the journal Physics of Fluids and can be accessed here.
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