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Google, Harvard to use machine learning to predict earthquake aftershock locations

Although the timing and size of aftershocks has been understood and explained by established empirical laws, forecasting the locations of these events has proven more challenging.

  • Published: August 31, 2018 4:30 PM IST
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Scientists from Harvard have teamed up with tech giant Google for using machine learning to predict where earthquake aftershocks may occur, a move that will help minimise impact of the natural disaster on lives and infrastructure in the coming years. Also Read - UEFA Euro 2020: Colourful Google Doodle kicks off European Football Championship

Earthquakes typically occur in sequences — an initial “mainshock” followed by a set of “aftershocks.” While these aftershocks are usually smaller in magnitude, they may significantly hamper recovery efforts. Also Read - Android 12 beta 2 rolling out: New privacy features, tweaked design and more

Although the timing and size of aftershocks has been understood and explained by established empirical laws, forecasting the locations of these events has proven more challenging, Phoebe DeVries, Post-Doctoral Fellow at Harvard, said in a Google blogpost. Also Read - Sundar Pichai: 5 interesting facts about Google CEO you never heard before

Machine learning-based forecasts may, one day, help deploy emergency services and inform evacuation plans for areas at risk of an aftershock, she added.

“We teamed up with machine learning experts at Google to see if we could apply deep learning to explain where aftershocks might occur… We are looking forward to seeing what machine learning can do in the future to unravel the mysteries behind earthquakes, in an effort to mitigate their harmful effects,” she said.

DeVries outlined that the teams started with a database of information on more than 118 major earthquakes from around the world, including the one in Bhuj in 2001.

A “neural net” was then applied to analyse the relationships between static stress changes caused by the mainshocks and aftershock locations, and the algorithm was able to identify useful patterns.

“The end result was an improved model to forecast aftershock locations and while this system is still imprecise, it’s a motivating step forward,” she said.

DeVries said the process also helped identify physical quantities that may be important in earthquake generation, which opens up new possibilities for finding potential physical theories that may allow for better understanding this natural phenomena.

This is published unedited from the PTI feed.

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  • Published Date: August 31, 2018 4:30 PM IST



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