Machine learning could lead to more accurate earthquake prediction
LONDON, (Xinhua) — Researchers have trained a machine learning algorithm to successfully predict earthquake in a lab setting, and the techniques could be developed into a solution for real earthquake forecast, according to a study released Monday by the University of Cambridge.
Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
Researchers from University of Cambridge, Los Alamos National Laboratory and Boston University, analyzed the interactions among earthquakes, precursor quakes (often very small earth movements) and faults, with the hope of developing a method to predict earthquakes.
Using a lab-based system that mimics real earthquakes, the researchers trained a machine learning algorithm to predict future earthquakes by analyzing the acoustic signals coming from the fault as it moved and search for patterns.
The team believes that this sound pattern is a direct measure of the elastic energy that is in the system at a given time.
The characteristics of this sound pattern can be used to give a precise estimate of the stress on the fault and to estimate the time remaining before failure, which gets more and more precise as failure approaches, according to the study.
“This is the first time that machine learning has been used to analyze acoustic data to predict when an earthquake will occur, long before it does, so that plenty of warning time can be given — it’s incredible what machine learning can do,” said co-author Sir Colin Humphreys of the University of Cambridge.
Although the researchers caution that there are multiple differences between a lab-based experiment and a real earthquake, they hope to progressively scale up their approach by applying it to real systems which most resemble their lab system.
The study has been published in the journal Geophysical Review Letters.