An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia

Abstract

Slow slip events result from the spontaneous weakening of the subduction megathrust and bear strong resemblance to earthquakes, only slower. This resemblance allows us to study fundamental aspects of nucleation that remain elusive for classic, fast earthquakes. We rely on machine learning algorithms to infer slow slip timing from statistics of seismic waveforms. We find that patterns in seismic power follow the 14-month slow slip cycle in Cascadia, arguing in favor of the predictability of slow slip rupture. Here, we show that seismic power exponentially increases as the slowly slipping portion of the subduction zone approaches failure, a behavior that shares a striking similarity with the increase in acoustic power observed prior to laboratory slow slip events. Our results suggest that the nucleation phase of Cascadia slow slip events may last from several weeks up to several months.

Learning the Low Frequency Earthquake Activity on the Central San Andreas Fault

“…The problem we describe is challenging because of the spatially synchronous behavior of LFE families that can produce simultaneous emissions at unrelated locations (Trugman et al., 2015), and the frequent earthquakes occurring along the creeping section of the fault. For these reasons the central SAF presents unique conditions in contrast to other regions where related problems are explored, for example, tremor and slow‐slip in Cascadia (Hulbert et al., 2020; Rouet‐Leduc et al., 2019, 2020). Nevertheless, the model performance suggests the selected features are sufficient to characterize LFE behavior related to the evolving fault system.…”

“…Machine learning (ML) models are able to predict the timing of laboratory earthquakes (P. A. Johnson et al., 2021; Rouet‐Leduc et al., 2017) and quantify the physics prior to a slip event (Hulbert et al., 2019; Rouet‐Leduc et al., 2018). In the Cascadia subduction zone, ML models are able to increase the detection potential of tremors (Rouet‐Leduc et al., 2020), estimate the GPS measured surface displacement (Hulbert et al., 2020), and identify energy released before slow‐slip events (Hulbert et al., 2020). In this study, we develop a gradient boosted tree ML regression model to estimate LFE activity on the SAF.…”

Deep Learning Can Predict Laboratory Quakes From Active Source Seismic Data

“…Supervised approaches have shown that the timing (Hulbert et al., 2019; Rouet‐Leduc et al., 2017), shear stress (Hulbert et al., 2019; Rouet‐Leduc et al., 2018) as well as magnitude and duration (Hulbert et al., 2019) of laboratory earthquakes could be well predicted, and that the variance of AE time‐series was by far the most predictive feature. These ML approaches have also been tested on field data (slow slip sequences along the Cascadia Subduction zone (Hulbert et al., 2020; Rouet‐Leduc et al., 2019, 2020). Beyond the direct applicability to field prediction of seismic events (Niu et al., 2008), these approaches are of great interest at the laboratory scale, to help unravel the mechanisms that dictate earthquake nucleation.…”

Laboratory earthquake forecasting: A machine learning competition

“…This rapid expansion has seen application of existing and new ML tools to a suite of geoscientific problems ( 33 – 36 ) that span seismic wave detection and phase identification and location ( 34 , 37 – 45 ), geological formation identification ( 46 , 47 ), earthquake early warning ( 48 ), volcano monitoring ( 49 – 51 ), denoising Interferometric Synthetic Aperture Radar (InSAR) ( 50 , 52 , 53 ), tomographic imaging ( 54 – 57 ), reservoir characterization ( 58 – 60 ), and more. Of particular note is that, over the past 5 y, considerable effort has been devoted to using these approaches to characterize fault physics and forecast fault failure ( 1 – 3 , 13 , 35 , 61 – 63 ).…”

“…4 and are from ref. 61 . This plot shows the ML slip timing estimates (in blue) and the time remaining before the next slow slip event (ground truth; dashed red lines).…”