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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."
Acoustic signals generated in the early stages of impending fast laboratory earthquakes are systematically larger than those for slow slip events. Here, we show that a broad range of stick–slip and creep–slip modes of failure can be predicted and share common mechanisms, which suggests that catastrophic earthquake failure may be preceded by an organized, potentially forecastable, set of processes.
Here we show that the Cascadia subduction zone is apparently continuously broadcasting a low-amplitude, tremor-like signal that precisely informs of the fault displacement rate throughout the slow slip cycle. Using a method based on machine learning previously developed in the laboratory, we analysed large amounts of raw seismic data from Vancouver Island to separate this signal from the background seismic noise. We posit that this provides indirect real-time access to fault physics on the down-dip portion of the megathrust, and thus may prove useful in determining if and how a slow slip may couple to or evolve into a major earthquake.
Random Forest (RF) approach for predicting time remaining before failure. (a) Shear stress (black curve) exhibits sharp drops, indicating failure events (laboratory earthquakes). We wish to predict the time remaining before the next failure derived from the shear stress drops (red curve), using only the (b) acoustic emission (dynamic strain) data. The dashed rectangle represents a moving time window; each window generates a single point on each feature curve below (e.g., variance and kurtosis). (c) The RF model predicts the time remaining before the next failure by averaging the predictions of 1,000 decision trees for each time window. Each tree makes its prediction (white leaf node), following a series of decisions (colored nodes) based on features of the acoustic signal during the current window (see supporting information S1). (d) The RF prediction (blue line) on data it has never seen (testing data) with 90% confidence intervals (blue shaded region). The predictions agree remarkably well with the actual remaining times before failure (red curve). We emphasize that the testing data are entirely independent of the training data, and were not used to construct the model. Data are from experiment number p2394.