TOKYO, Aug 30, 2018 (BSS/AFP) – Lightning might not strike twice, but
earthquakes can. And forecasting where aftershocks will hit might now be a
little easier thanks to an assist from artificial intelligence.
Aftershocks can be more destructive than the quakes they follow, making it
all the more important for experts to be able to predict them.
But while seismologists have methods to forecast when aftershocks will hit
and how strong they will be, there is more uncertainty about how to predict
where they will strike.
Hoping to address that, a group of researchers trained a “deep learning”
programme with data about tens of thousands of earthquakes and aftershocks to
see if they improve predictions.
“The previous baseline for aftershock forecasting has a precision of
around three percent across the testing data set. Our neural network approach
has a precision of around six percent,” said Phoebe DeVries, co-author of the
study published in the journal Nature on Thursday.
“This approach is more accurate because it was developed without a
strongly held prior belief about where aftershocks ought to occur,” DeVries,
a post-doctoral fellow at Harvard, told AFP.
The researchers used a type of artificial intelligence known as deep
learning, which is loosely modelled on the way the human brain makes
connections.
The programme allowed the researchers to map relationships “between the
characteristics of a large earthquake — the shape of the fault, how much did
it slip, and how did it stress the earth — and where aftershocks occurred,”
said Brendan Meade, professor of earth and planetary science at Harvard, and
a study co-author.
The researchers tested the network by holding back a quarter of their data
set, and feeding the remaining information into the programme.
They then tested how well the programme predicted the aftershock locations
of the 25 percent of cases it hadn’t been fed.
They found six percent of the areas the programme identified as high-risk
did in fact experience aftershocks, up from three percent using existing
methods.
Analysing the research, Gregory Beroza, a professor of geophysics at
Stanford University, cautioned it “might be premature to infer… an improved
physical understanding of aftershock triggering”.
In an article published in Nature alongside the study, he said the
research had focused on only one set of changes caused by earthquakes that
can affect where aftershocks occur.
“Another reason for caution is that the authors’ analysis relies on
factors that are fraught with uncertainty,” Beroza wrote.
DeVries acknowledged that additional factors affect where aftershocks
occur and that there is “much more to be done”.
“We definitely agree that this work is a motivating beginning, rather than
an ending,” she said.
And Beroza said the research had established a “beachhead” for additional
study into how artificial intelligence could help forecasting.
“The application of machine-learning methods has the potential to extract
meaning from these large and complex sources of information, but we are still
in the early stages of this process.”