P08

Machine Learning Predicts Laboratory Earthquakes in a Meter-scale Rock-friction Experiment
Reiju NORISUGI, 〇Yoshihiro KANEKO, Bertrand ROUET-LEDUC

Machine learning (ML) has been used to study the predictability of laboratory earthquakes. However, thequestion remains whether or not this approach can be applied in a tectonic setting where one may have to rely onsparse earthquake catalogs, and where important timescales vary by orders of magnitude. We first applied ML to asynthetic seismicity catalog, generated by continuum models of a rate-and-state fault with frictional heterogeneities,
containing foreshocks, mainshocks, and aftershocks that nucleate in a similar manner. We developed a network
representation of the seismicity catalog to calculate the input features and found that the trained ML model canpredict the time to mainshock with great accuracy, from the scale of decades to minutes. Based on the simulation
results, we then applied our method to the data from a meter-scale rock-friction laboratory experiment. Remarkably,
we found that the trained ML model can predict the time to failure with great accuracy, from the scale of tens ofseconds down to milliseconds.
1. Introduction

2. Materials & Method

Recent studies demonstrated that machine learning

We use a catalog and shear stress data in a meter-(ML) can predict laboratory fast and slow slip events,

scale rock-friction experiment (Yamashita et al., 2021).

through instantaneous monitoring of continuous

Figure 1 shows the schematic of the experimental

acoustic emissions (AEs). For example, Rouet-Leducfacilities (Figure from Yamashita et al., 2021). Weet al. (2017) showed that the statistical features of AEs,

developed a new approach, “network representation”

notably the evolution of their variance, provide(Figure 2), to quantify the earthquake pattern by using

predictability of the time to failure in the double direct-

only catalog information (hypocenter, magnitude, andshear experiment. While the study has provided new

event origin time). We define a network as a group of

insights into the slip phenomenon on a fault plane, there

specific numbers of consecutive earthquakes andare at least two major questions to apply the ML-based

abstract the catalog information by computing statistics

approach to forecast real earthquakes, as mentioned inof catalogs within each network. ML is trained using

the abstract. To partially address these issues, we firstthe abstracted catalog information with the time

use a 2D fully-dynamic earthquake-cycle simulation

remaining before the next laboratory failure. Thus, thewith regular frictional heterogeneity on the 1D fault

trained ML model attempts to predict future(Ito & Kaneko, 2023) and develop the catalog-based

information, the time to failure, by monitoring theML approach to predict multi-scale mainshock cycles.

current and past earthquake patterns. In addition to

Based on the simulation work, in this study, we apply

predicting the time to failure, we also attempt to predict

our developed method to the catalog of a meter-scale

the shear stress data by monitoring the foreshockrock-friction experiment (Yamashita et al., 2021) andactivity, as tested in previous works (e.g., Rouet-Leductest whether or how it works.

et al., 2018; Shreedharan et al., 2021).
1Based on the output of the trained ML model, theincrease in seismic moment (or magnitude) and thedecrease in recurrence interval of events averaged overcertain

networks

providethepredictability

oflaboratory earthquakes and shear stress. These features
will correspond to the decrease in the b-value or theacceleration of the event rate before the laboratory

Figure 1: A schematic illustration of the experimental

failure, as observed in Yamashita et al., 2021. Ourfacility used in Yamashita et al., 2021.

Distance along fault

Network moves

approach to quantifying the earthquake pattern cancapture the precursory foreshock activity, and this is theNext networkby one earthquakereason why ML can predict laboratory earthquakes andCurrent network

shear stress in this study. Ongoing work is traying to
speculate the physical mechanism of predictability byΔx

combining the simulation and experimental results.

ΔtTimeTime to filure

4. Conclusions

Figure 2: A schematic illustration of network

We showed that the trained machine-learning model

representation. Earthquake groups enclosed by green

by monitoring the foreshock activity can accuratelyand orange lines correspond to the current and next

predict the time to mainshock and shear stress in anetwork. The catalog information is abstracted in eachmeter-scale rock-friction laboratory experiment. Thenetwork and connected with the time to failure.

increase in seismic moment and the decrease in event
recurrence averaged over certain networks have the3. Result & Discussion

predictive power of time to failure and shear stress. Our

Figure 3 shows that the trained ML model can

result will offer clues as to why machine learning canaccurately predict both the time remaining before thepredict laboratory earthquakes.

next laboratory failure (future information) and shear
stress (current fault physical state).

References・Ito & Kaneko. (2023).
Journal of Geophysical Research: Solid Earth, 128(12),e2023JB027413.

・Rouet‐Leduc et al. (2017).
Geophysical Research Letters, 44(18), 9276-9282.

・Rouet‐Leduc et al. (2018).
Geophysical Research Letters, 45(3), 1321-1329.
・Shreedharan et al. (2021).
Journal of Geophysical Research: Solid Earth, 126(7),e2020JB021588.

Figure 3: Prediction of time to failure (a) and detrended

・Yamashita et al. (2021).

shear stress (b) by the trained ML model. We evaluate

Nature communications, 12(1), 4302.

the performance by the coefficient of determination R2.
2