From: Los Alamos National Laboratory
Published August 30, 2017 03:14 PM

Machine-learning earthquake prediction in lab shows promise

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 in Geophysical 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.”

Continue reading at Los Alamos National Laboratory

Image via Los Alamos National Laboratory

Terms of Use | Privacy Policy

2017©. Copyright Environmental News Network