"The deep-learning transformer model we used is synonymous with a language translation model, such as Google Translate, using a codebook to translate a sentence to a different language," said Chris Johnson.
In a novel approach, the Los Alamos team applied a deep-learning transformer model to acoustic emissions broadcast from the laboratory fault to predict the frictional state. "The acoustic signals emitted by the laboratory fault contain foreshadowing information about the future fundamental physics of the system through the entire earthquake cycle and beyond, as we now show," Paul Johnson said.
Paul Johnson, corresponding author of the paper, geophysicist and Laboratory fellow at Los Alamos National Laboratory, leads a team that has made steady advances in applying various machine-learning techniques to the challenge of forecasting earthquakes in the laboratory and in the field. That's never been done, and it provides a potential path to near-term forecasting of earthquake timing in Earth," said Chris Johnson, co-lead author of a paper on the findings in Geophysical Research Letters.
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