Science Research] By listening to the acoustic signal emitted by a laboratory-created earthquake, a computer science approach using machine learning can predict the time remaining before the fault fails.
“At any given instant, the noise coming from the lab fault zone
provides quantitative information on when the fault will slip,” said
Paul Johnson, a Los Alamos National Laboratory fellow and lead investigator on the research, which was published today inGeophysical Research Letters.
“The novelty of our work is the use of machine learning to discover
and understand new physics of failure, through examination of the
recorded auditory signal from the experimental setup. I think the future
of earthquake physics will rely heavily on machine learning to process
massive amounts of raw seismic data. Our work represents an important
step in this direction,” he said.
Not only does the work have potential significance to earthquake
forecasting, Johnson said, but the approach is far-reaching, applicable
to potentially all failure scenarios including nondestructive testing of
industrial materials brittle failure of all kinds, avalanches and other events.
Machine learning is an artificial intelligence approach to allowing the computer to learn from new data, updating its own results to reflect the implications of new information.
The machine learning technique used in this project also identifies new signals, previously thought to be low-amplitude noise, that provide forecasting information throughout the earthquake cycle. “These signals resemble Earth
tremor that occurs in association with slow earthquakes on tectonic
faults in the lower crust,” Johnson said. “There is reason to expect
such signals from Earth faults in the seismogenic zone for slowly slipping faults.”
Machine learning algorithms can predict failure times of laboratory
quakes with remarkable accuracy. The acoustic emission (AE) signal,
which characterizes the instantaneous physical
state of the system, reliably predicts failure far into the future.
This is a surprise, Johnson pointed out, as all prior work had assumed
that only the catalog of large events is relevant, and that small
fluctuations in the AE signal could be neglected.
To study the phenomena, the team analyzed data from a laboratory
fault system that contains fault gouge, the ground-up material created
by the stone blocks sliding past one another. An accelerometer recorded
the acoustic emission emanating from the shearing layers. Read More