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An imperfect mimic
In addition to spotting overlooked patterns, machine learning can also help to tame overwhelming data sets. Modeling how an earthquake affects the viscous part of the layer in Earth’s interior that extends hundreds of miles below the planet’s outermost crust, for example, requires an insurmountably large amount of computing power. But machine learning algorithms can find shortcuts, essentially mimicking solutions to more detailed equations with less computing.
“We can get a pretty good approximation to reality, which we’ll be able to apply to data sets that are so big or simulations that are so extensive, that the most powerful computers available would not be able to process them,” Beroza said.
What’s more, any shortfalls in the precision of artificial intelligence-based solutions to these equations often pale in significance compared to the influence of scientists’ own decisions about how to set up calculations in the first place. “Our largest source of error comes not from our inability to solve the equations,” Beroza said. “It comes from knowing what the interior structure of the Earth is really like and the parameters that should go into those equations.”
To be sure, machine learning is far from a perfect tool for answering the thorniest questions in Earth science. “The most powerful machine-learning algorithms typically require large labeled data sets, which are not available for many geoscience applications,” Bergen said. If scientists train an algorithm on insufficient or improperly labeled data, she warned, it can cause models to reproduce biases that don’t necessarily reflect reality.
This type of error can be combatted in part through greater transparency and creation of “benchmark” data sets, which the researchers argue can spur competition and allow for apples-to-apples comparisons of algorithm performance. According to Bergen, “Adoption of open science principles, including sharing of data and code, will help to accelerate research and also allow the community to identify and address limitations or weaknesses of proposed approaches.”
Human impatience may be harder to keep in check. “What I’m worried about is that people are going to use AI naively,” Beroza said. “You could imagine someone training a many-layer, deep neural network to do earthquake prediction – and then not testing the method in a way that properly validates its predictive value.”
Co-authors are affiliated with Los Alamos National Laboratory and Rice University.
The work was supported by the U.S. Department of Energy, the National Science Foundation, the Harvard Data Science Initiative, Los Alamos National Laboratory and the Simons Foundation.
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