AI predicts future slip physics in lab earthquakes – ScienceDaily

AI approaches borrowed from natural language processing — such as language translation and text auto-filling on your smartphone — can predict future fault friction and the next failure time with high accuracy in laboratory earthquakes. The technology, which applies artificial intelligence to the audio signals of a fault, advances past work and goes beyond by predicting aspects of the future state of the fault’s physical system.

said Chris Johnson, co-lead author of a paper on the findings in Geophysical Research Letters.

Paul Johnson, corresponding author of the research paper, geophysicist and lab fellow at Los Alamos National Laboratory, leads a team that has made steady progress in applying various machine learning techniques to the challenge of earthquake prediction in the lab and in the field.

“The acoustic signals emitted by the lab fault contain information that foreshadows the future fundamental physics of the system during the entire earthquake cycle and beyond, as we now demonstrate,” said Paul Johnson. “This has never been seen before.”

In a new approach, the Los Alamos team applied a deep learning transducer model of acoustic emissions beamed from a laboratory error to predict the state of friction.

“The deep learning converter model we used is synonymous with a language translation model, like Google Translate, using a code book to translate a sentence into a different language,” said Chris Johnson. “You can think of this as writing an email in English and having the AI ​​translate from English to Japanese while also anticipating your words and autofilling them at the end of the sentence.”

Chris Johnson said AI “takes data on what’s happening now and says what happens next on the wrong.”

The Los Alamos team had previously predicted the timing of failures in laboratory earthquakes and in historical slow-slip Earth data using a number of machine learning techniques. Applying machine learning to data from laboratory shear experiments showed that fault emissions are imprinted with information regarding their current state and place in the slip cycle.

Indeed, the statistical properties of the fault’s continuous seismic signal identified through machine learning allowed Los Alamos researchers to predict instantaneous evolving—not future—friction, friction and other attributes, along with the timing of the fault. Next lab earthquake.

In this previous work, waveform, or acoustic emission, data is fed into a model to predict the current state of a fault system. This forecast includes an estimate of the countdown, or time of failure, of the next slip event, with some degree of uncertainty, which is not a future prediction but a description of the current state of the system.

“Now we are making future prediction from past data, which goes beyond describing the instantaneous state of the system. The model learns from waveforms to predict future fault friction and when the next slip event will occur using only past information, without using any data from the significant future time step,” he said. Chris Johnson.

“The model is not constrained by physics, but it does predict the physics, and the actual behavior of the system,” Chris Johnson said.

“The next challenge is whether we can do this in the ground to predict the displacement of errors in the future, for example,” said Paul Johnson. “This is an open question, because we don’t have the long data sets to train the model as we do in the lab.”

The method can be applied to other disciplines, such as testing for non-destructive materials, where it can provide information about gradual damage and imminent damage to a bridge, for example.

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