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  • 1
    Publication Date: 2021-07-03
    Description: Subduction zones are monitored using space geodesy with increasing resolution, with the aim of better capturing the deformation accompanying the seismic cycle. Here, we investigate data characteristics that maximize the performance of a machine learning binary classifier predicting slip‐event imminence. We overcome the scarcity of recorded instances from real subduction zones using data from a seismotectonic analog model monitored with a spatially dense, continuously recording onshore geodetic network. We show that a 70–85 km‐wide coastal swath recording interseismic deformation gives the most important information on slip imminence. Prediction performances are mainly influenced by the alarm duration (amount of time that we consider an event as imminent), with density of stations and record length playing a secondary role. The techniques developed in this study are most likely applicable in regions of slow earthquakes, where stick‐slip‐like failures occur at time intervals of months to years.
    Description: Plain Language Summary: Machine learning, a group of algorithms that produce predictions based on past “experience,” has been successfully used to predict various aspects of the earthquake process, including slip imminence. The accuracy of those algorithms depends on a variety of data characteristics, for example, the amount of data used for building the “experience” of the model. We focus on this point using a scaled representation of a seismic subduction zone and a monitoring technique similar to Global Navigation Satellite System. We identify the most useful surface regions to be monitored and the parameter that most strongly influences prediction accuracy for the timing of upcoming laboratory earthquakes. The routine implemented in this study could be used to predict the onset and extent of slow earthquakes.
    Description: Key Points: We investigate the performances of a binary classifier predicting slip‐event imminence in analog models of megathrust seismic cycling. A 70–85 km‐wide coastal swath is the region producing the most important information for the imminence classification. Length of time that we consider an event imminent plays a primary role in tuning the performances of a binary classifier predicting the imminence of analog earthquakes.
    Description: DAAD‐Prime
    Keywords: 551.22 ; megathrust earthquakes ; machine learning ; analog modeling
    Type: article
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  • 2
    Publication Date: 2022-03-25
    Description: Currently, it is unknown how seismic and aseismic slip influences the recurrence and magnitude of earthquakes. Modern seismic hazard assessment is therefore based on statistics combined with numerical simulations of fault slip and stress transfer. To improve the underlying statistical models we conduct low velocity shear experiments with glass micro‐beads as fault gouge analogue at confining stresses of 5–20 kPa. As a result, we show that characteristic slip events emerge, ranging from fast and large slip to small scale oscillating creep and stable sliding. In particular, we observe small scale slip events that occur immediately before large scale slip events for a specific set of experiments. Similar to natural faults we find a separation of scales by several orders of magnitude for slow events and fast events. Enhanced creep and transient dilatational events pinpoint that the granular analogue is close to failure. From slide‐hold‐slide tests, we find that the rate‐and‐state properties are in the same range as estimates for natural faults and fault rocks. The fault shows velocity weakening characteristics with a reduction of frictional strength between 0.8% and 1.3% per e‐fold increase in sliding velocity. Furthermore, the slip modes that are observed in the normal shear experiments are in good agreement with analytical solutions. Our findings highlight the influence of micromechanical processes on macroscopic fault behavior. The comprehensive data set associated with this study can act as a benchmark for numerical simulations and improve the understanding of observations of natural faults.
    Description: Plain Language Summary: Earthquakes occur when two continental plates slide past each other. The motion is concentrated at the interface of the two plates which is called a fault. In many cases the fault is filled with granular material, called gouge, that supports the pressure between the plates. Therefore, the properties of this gouge determine how fast and how large an earthquake can be. It also has an influence on the time between earthquakes. In our study, we examine a simplified version of a fault gouge in a simple small‐scale model. Instead of rock material we use glass beads and measure how different conditions affect the motion of the model. We find that our model reproduces features of fault gouge because it shows similar behavior. When there is no motion our model fault becomes stronger with a rate equal to fault gouge. Also, the type of strengthening is analogous to fault gouge. During slip, the glass beads become weaker as the slip velocity increases in a similar manner as in natural faults. These results improve the understanding of computer simulations and natural observations.
    Description: Key Points: Slip modes in granular gouge are akin to natural fault slip. Glass beads are a suitable granular analogue for fault gouge and show rate‐and‐state dependent friction. Enhanced creep and small scale events are signals for imminent failure and indicate fault criticality.
    Description: Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659
    Description: 亥姆霍兹联合会致力, Helmholtz‐Zentrum Potsdam ‐ Deutsches GeoForschungsZentrum GFZ (GFZ) http://dx.doi.org/10.13039/501100010956
    Keywords: ddc:550.78
    Language: English
    Type: doc-type:article
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