Researchers at Los Alamos National Laboratory recently applied machine-learning to forecast earthquakes along Cascadia, which is located within the western region of North America.

After analyzing Cascadia data, the team of researchers discovered the megathrust broadcasts a constant tremor, a fingerprint of the fault’s displacement. It is interesting to note here that the team found a link between the loudness of the fault’s acoustic signal and its physical changes, said a Phys.org report.

“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,” Paul Johnson, Los Alamos scientist, was quoted as saying.

“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,” Johnson added.

What machine learning actually does is to chomp massive seismic data sets to obtain distinct patterns. This is done by the way of learning from self-adjusting algorithms to generate decision trees that choose and retest a series of questions and answers.