The team combined state-of-the-art acoustic technology with AI tools to monitor tectonic activity in real time and improve the accuracy of existing tsunami alerts.
To train the AI model, the researchers relied on sound recordings captured by underwater microphones, called hydrophones. These sounds were used to measure the acoustic radiation produced by 200 earthquakes that happened in the Pacific and Indian Oceans.
Then, the researchers leveraged an AI computational model to triangulate the source of the tectonic event and classify earthquakes' properties, including length and width, uplift speed, and duration, which are used to reveal the size of the tsunami.
"Tsunamis can be highly destructive events causing huge loss of life and devastating coastal areas, resulting in significant social and economic impacts as whole infrastructures are wiped out," said Dr Usama Kadri, a co-author of the research.
"Our study demonstrates how to obtain...