AI Case Study
Researchers at Los Alamos National Laboratory identified a sound pattern that indicates slippage and fault failure using machine learning
Researchers at Los Alamos National Laboratory's Earth and Environmental Sciences and Intelligence and Space Research divisions, and at Pennsylvania State University studied Cascadia, a 700-mile-long fault from northern California to southern British Columbia. With the use of machine learning, the researchers were able to analyse 12 years of real data from the region's seismic stations and identify acoustic signals that indicate slippage and fault failure. This is crucial as these elements have been closely related to earthquakes in other subduction zones.
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"Machine-learning research published in two related papers today in Nature Geoscience reports the detection of seismic signals accurately predicting the Cascadia fault's slow slippage, a type of failure observed to precede large earthquakes in other subduction zones.
Los Alamos National Laboratory researchers applied machine learning to analyze Cascadia data and discovered the megathrust broadcasts a constant tremor, a fingerprint of the fault's displacement. More importantly, they found a direct parallel between the loudness of the fault's acoustic signal and its physical changes. Cascadia's groans, previously discounted as meaningless noise, foretold its fragility.
"Cascadia's behavior was buried in the data. Until machine learning revealed precise patterns, we all discarded the continuous signal as noise, but it was full of rich information. We discovered a highly predictable sound pattern that indicates slippage and fault failure," said Los Alamos scientist Paul Johnson. "We also found a precise link between the fragility of the fault and the signal's strength, which can help us more accurately predict a megaquake."
"The team analyzed 12 years of real data from seismic stations in the region and found similar signals and results: Cascadia's constant tremors quantify the displacement of the slowly slipping portion of the subduction zone. In the laboratory, the authors identified a similar signal that accurately predicted a broad range of fault failure. Careful monitoring in Cascadia may provide new information on the locked zone to provide an early warning system."
"Machine learning crunches massive seismic data sets to find distinct patterns by learning from self-adjusting algorithms to create decision trees that select and retest a series of questions and answers."
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"Recent research reveals that Cascadia has been active, but noted activity has been seemingly random."
12 years of real data from seismic stations in the region.