AI predicts physics of future fault slip in laboratory earthquakes
Analyzing seismic signals, a natural-language processing approach ‘auto-fills’ the future physical state of a fault in an earthquake machine, with potential applications in Earth.
An artificial-intelligence approach borrowed from natural-language processing — much like language translation and auto-fill for text on your smartphone — can predict future fault friction and the next failure time with high resolution in laboratory earthquakes. The technique, applying AI to the fault’s acoustic signals, advances previous 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 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.
Like a language translation model
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.
Chris Johnson said the AI “takes data of what’s happening right now and says what’s happening next on the fault.”
The Los Alamos team had previously forecasted fault failure timing in laboratory quakes and in historical slow-slip Earth data using a number of machine-learning techniques. Applying machine learning to data from laboratory shear experiments demonstrated that the fault emissions are imprinted with information regarding its current state and where it is in the slip cycle.
Indeed, the statistical features of the continuous seismic signal emitted from the fault and identified by machine learning allowed the Los Alamos researchers to predict the evolving instantaneous — but not future — fault friction, displacement and other characteristics, along with the timing of the next lab quake.
In that previous work, the waveform, or acoustic emission, data is input to a model to predict the current state of the fault system. That prediction includes a countdown estimate, or time to failure, for 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.
The method could be applied to other disciplines, such as nondestructive materials testing, where it could provide information about progressive damage and impending damage to, say, a bridge.