Precursory changes in seismic velocity for the spectrum of earthquake failure modes

Evolution of force networks during stick-slip motion of an intruder in a granular material: Topological measures extracted from experimental data

In quasi-two-dimensional experiments with photoelastic particles confined to an annular region, an intruder constrained to move in a circular path halfway between the annular walls experiences stick-slip dynamics. We discuss the response of the granular medium to the driven intruder, focusing on the evolution of the force network during sticking periods. Because the available experimental data do not include precise information about individual contact forces, we use an approach developed in our previous work [Basak et al., J. Eng. Mech. 147, 04021100 (2021)0733-939910.1061/(ASCE)EM.1943-7889.0002003] based on networks constructed from measurements of the integrated strain magnitude on each particle. These networks are analyzed using topological measures based on persistence diagrams, revealing that force networks evolve smoothly but in a nontrivial manner throughout each sticking period, even though the intruder and granular particles are stationary. Characteristic features of persistence diagrams show identifiable slip precursors. In particular, the number of generators describing the structure and complexity of force networks increases consistently before slips. Key features of the dynamics are similar for granular materials composed of disks or pentagons, but some details are consistently different. In particular, we find significantly larger fluctuations of the measures computed based on persistence diagrams and, therefore, of the underlying networks, for systems of pentagonal particles.

Foreshock properties illuminate nucleation processes of slow and fast laboratory earthquakes

Understanding the connection between seismic activity and the earthquake nucleation process is a fundamental goal in earthquake seismology with important implications for earthquake early warning systems and forecasting. We use high-resolution acoustic emission (AE) waveform measurements from laboratory stick-slip experiments that span a spectrum of slow to fast slip rates to probe spatiotemporal properties of laboratory foreshocks and nucleation processes. We measure waveform similarity and pairwise differential travel-times (DTT) between AEs throughout the seismic cycle. AEs broadcasted prior to slow labquakes have small DTT and high waveform similarity relative to fast labquakes. We show that during slow stick-slip, the fault never fully locks, and waveform similarity and pairwise differential travel times do not evolve throughout the seismic cycle. In contrast, fast laboratory earthquakes are preceded by a rapid increase in waveform similarity late in the seismic cycle and a reduction in differential travel times, indicating that AEs begin to coalesce as the fault slip velocity increases leading up to failure. These observations point to key differences in the nucleation process of slow and fast labquakes and suggest that the spatiotemporal evolution of laboratory foreshocks is linked to fault slip velocity.

Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes

Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction.

Stochastic methods for slip prediction in a sheared granular system

We consider a sheared granular system experiencing intermittent dynamics of stick-slip type via discrete element simulations. The considered setup consists of a two-dimensional system of soft frictional particles sandwiched between solid walls, one of which is exposed to a shearing force. The slip events are detected using stochastic state space models applied to various measures describing the system. The amplitudes of the events spread over more than four decades and present two distinctive peaks, one for the microslips and the other for the slips. We show that the measures describing the forces between the particles provide earlier detection of an upcoming slip event than the measures based solely on the wall movement. By comparing the detection times obtained from the considered measures, we observe that a typical slip event starts with a local change in the force network. However, some local changes do not spread globally over the force network. For the changes that become global, we find that their size strongly influences the further behavior of the system. If the size of a global change is large enough, then it triggers a slip event; if it is not, then a much weaker microslip follows. Quantification of the changes in the force network is made possible by formulating clear and precise measures describing their static and dynamic properties.

Seeking Repeating Anthropogenic Seismic Sources: Implications for Seismic Velocity Monitoring at Fault Zones

Seismic velocities in rocks are highly sensitive to changes in permanent deformation and fluid content. The temporal variation of seismic velocity during the preparation phase of earthquakes has been well documented in laboratories but rarely observed in nature. It has been recently found that some anthropogenic, high-frequency (>1 Hz) seismic sources are powerful enough to generate body waves that travel down to a few kilometers and can be used to monitor fault zones at seismogenic depth. Anthropogenic seismic sources typically have fixed spatial distribution and provide new perspectives for velocity monitoring. In this work, we propose a systematic workflow to seek such powerful seismic sources in a rapid and straightforward manner. We tackle the problem from a statistical point of view, considering that persistent, powerful seismic sources yield highly coherent correlation functions (CFs) between pairs of seismic sensors. The algorithm is tested in California and Japan. Multiple sites close to fault zones show high-frequency CFs stable for an extended period of time. These findings have great potential for monitoring fault zones, including the San Jacinto Fault and the Ridgecrest area in Southern California, Napa in Northern California, and faults in central Japan. However, extra steps, such as beamforming or polarization analysis, are required to determine the dominant seismic sources and study the source characteristics, which are crucial to interpreting the velocity monitoring results. Train tremors identified by the present approach have been successfully used for seismic velocity monitoring of the San Jacinto Fault in previous studies.

The High-Frequency Signature of Slow and Fast Laboratory Earthquakes

Tectonic faults fail through a spectrum of slip modes, ranging from slow aseismic creep to rapid slip during earthquakes. Understanding the seismic radiation emitted during these slip modes is key for advancing earthquake science and earthquake hazard assessment. In this work, we use laboratory friction experiments instrumented with ultrasonic sensors to document the seismic radiation properties of slow and fast laboratory earthquakes. Stick-slip experiments were conducted at a constant loading rate of 8 μm/s and the normal stress was systematically increased from 7 to 15 MPa. We produced a full spectrum of slip modes by modulating the loading stiffness in tandem with the fault zone normal stress. Acoustic emission data were recorded continuously at 5 MHz. We demonstrate that the full continuum of slip modes radiate measurable high-frequency energy between 100 and 500 kHz, including the slowest events that have peak fault slip rates <100 μm/s. The peak amplitude of the high-frequency time-domain signals scales systematically with fault slip velocity. Stable sliding experiments further support the connection between fault slip rate and high-frequency radiation. Experiments demonstrate that the origin of the high-frequency energy is fundamentally linked to changes in fault slip rate, shear strain, and breaking of contact junctions within the fault gouge. Our results suggest that having measurements close to the fault zone may be key for documenting seismic radiation properties and fully understanding the connection between different slip modes.

Potential seismic precursors and surficial dynamics of a deadly Himalayan disaster: an early warning approach

On 7 February 2021, Chamoli district (Uttarakhand, India) was devastated by a deadly rock-ice avalanche that led to a large causality of more than 200 people and a huge economic loss. We found noteworthy sequence of precursory signals of main failure/detachment preceded by a dynamic nucleation phase. The rock-ice avalanche appears to have been initiated by seismic precursors which were continuously active for 2:30 h prior to main detachment. The seismic amplitude, frequency characteristics and signal-to-noise ratio variation of detected tremors indicate static to dynamic changes in nucleation phase located at the source of detached wedge. The characteristics of seismic data distinguished debris flow and hitting obstacles from other seismic sources and allowed the estimations of debris flow speed. We analyzed and verified the seismic signals with field evidences to estimate the associated impacts and velocity of dynamic flow. The proximal high-quality seismic data allowed us to reconstruct the complete chronological sequence and evaluate impacts since the initiation of nucleation phase to its advancement. Furthermore, we suggest that real-time seismic monitoring with existing network and future deployment of integrated dense network can be used for forecasting of flow events and hazard mitigation in the downstream.

Predicting fault slip via transfer learning

Data-driven machine-learning for predicting instantaneous and future fault-slip in laboratory experiments has recently progressed markedly, primarily due to large training data sets. In Earth however, earthquake interevent times range from 10’s-100’s of years and geophysical data typically exist for only a portion of an earthquake cycle. Sparse data presents a serious challenge to training machine learning models for predicting fault slip in Earth. Here we describe a transfer learning approach using numerical simulations to train a convolutional encoder-decoder that predicts fault-slip behavior in laboratory experiments. The model learns a mapping between acoustic emission and fault friction histories from numerical simulations, and generalizes to produce accurate predictions of laboratory fault friction. Notably, the predictions improve by further training the model latent space using only a portion of data from a single laboratory earthquake-cycle. The transfer learning results elucidate the potential of using models trained on numerical simulations and fine-tuned with small geophysical data sets for potential applications to faults in Earth.

By teaching machine learning models with earthquake fault numerical simulations laboratory fault slip is predictable. Training the model further with a snippet of laboratory data improves predictions suggesting an approach to probing faults in Earth.

Frequency-Magnitude Statistics of Laboratory Foreshocks Vary With Shear Velocity, Fault Slip Rate, and Shear Stress

Understanding the temporal evolution of foreshocks and their relation to earthquake nucleation is important for earthquake early warning systems, earthquake hazard assessment, and earthquake physics. Laboratory experiments on intact rock and rough fractures have demonstrated that the number and size of acoustic emission (AE) events increase and that the Gutenberg-Richter b-value decreases prior to coseismic failure. However, for lab fault zones of finite width, where shear occurs within gouge, the physical processes that dictate temporal variations in frequency-magnitude (F/M) statistics of lab foreshocks are unclear. Here, we report on a series of laboratory experiments to illuminate the physical processes that govern temporal variations in b-value and AE size. We record AE data continuously for hundreds of lab seismic cycles and report F/M statistics. Our foreshock catalogs include cases where F/M data are not exponentially distributed, but we retain the concept of b-value for comparison with other works. We find that b-value decreases as the fault approaches failure, consistent with previous works. We also find that b-value scales inversely with shear velocity and fault slip rate, suggesting that fault slip acceleration during earthquake nucleation could impact foreshock F/M statistics. We propose that fault zone dilation and grain mobilization have a strong influence on foreshock magnitude. Fault dilation at higher shearing rates increases porosity and results in larger foreshocks and smaller b-values. Our observations suggest that lab earthquakes are preceded by a preparatory nucleation phase with systematic variations in AE and fault zone properties.

Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes

Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab-based studies have used acoustic emission data to predict time-to-failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active-source seismic data, which probe asperity-scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P-wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to "learn" about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics-informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.

Origin of the Co-Seismic Variations of Elastic Properties in the Crust: Insight From the Laboratory

Seismological observations highlighted that earthquakes are often followed by changes in elastic properties around the fault zone. Here, we studied the origin of these variations using stick-slip experiments on saw-cut granite samples presenting different degrees of bulk damage (i.e., microcracks). Stick-slip events were induced under triaxial compression configuration with continuous active ultrasonic measurements at confining pressures representative of upper crustal conditions (15-120 MPa). Both the P-wave velocity (V P ) and amplitude (A P ) showed drops, concurrently with stress drops, and had a non-monotonic dependence toward the fault's stress state. Our experimental results suggest that co-seismic changes inV P were mostly controlled by the elastic re-opening of microcracks in the bulk, rather than by co-seismic damage or the formation of fault gouge. Co-seismic changes inA P were controlled by a combination of elastic re-opening of microcracks in the bulk and inelastic processes (i.e., co-seismic damage and gouge formation and dilation).

Lithological and stress anisotropy control large-scale seismic velocity variations in tight carbonates

Our knowledge of subsurface structures often derives from seismic velocities that are measured during seismic acquisition surveys. These velocities can greatly change due to lithological, fracture frequencies and/or effective pressure/temperature variations. However, the influence of such intrinsic lithological properties and environmental conditions at the large scale is poorly understood due to the lack of comprehensive datasets. Here, we analyze 43 borehole-derived velocity datasets of 3 end-member tight carbonate sequences from Central Italy, including massive pure limestone (Calcare Massiccio, CM), thick-layered (20-50 cm) pure limestone (Maiolica, MA), and thin-layered (2-20 cm) marly limestone (Calcareous Scaglia, CS). Our results show that the main rock parameters and environmental conditions driving large scale velocity variations are bedding and paleostresses, while mineralogical composition and current tectonic stress also play a role. For each of the 3 end-members, measured V_P values vary differently with depth, as the thin-layered CS units show a clear increase in Vp, while velocity slightly increases and remains constant for the thick-layered MA and massive CM units, respectively. Such observations show that velocities are affected by specific characteristics of lithological discontinuities, such as the thickness of bedding. Counterintuitively, larger Vp values were recorded in the deformed mountain range than in the undeformed foreland suggesting that higher paleo-stresses increase velocity values by enhancing diagenesis and healing of discontinuities. Our results thus demonstrate that large scale velocity variations are strictly related to variation of lithological properties and to the geological and tectonic history of an area. We suggest that such lithological and environmental controls should be taken into account when developing velocity and mechanical models for tectonically active regions of the Mediterranean Area, where earthquakes mostly nucleate and propagate through carbonate formations, and for resource exploration in fractured carbonate reservoirs.

Laboratory earthquake forecasting: A machine learning competition

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

The source parameters of earthquakes of Bishkek geodynamic proving ground (Northern Tien Shan)

Based on the method of polarity of signs of P-waves, the focal mechanisms of 1674 earthquakes with M ≥ 1.6, which occurred on the territory of the Bishkek geodynamic proving ground (BGPG) from 1994 to 2020, were determined. Some characteristics of the complete catalogue are presented. Quantitative distributions by the type of mechanisms and diagrams of azimuths of the main stress axes are constructed. A variety of focal mechanism of earthquake is observed, most of them are reverse fault, oblique reverse fault, and horizontal strike-slip fault. The compression axis for most of the events has a north-northwest direction and a sub-horizontal position. For 183, dynamic parameters (DP, source parameters) were obtained: spectral density Ω0, corner frequency f0, scalar seismic moment M0, source radius (Brune radius) r, and stress drop Δσ. The correlations between DP and energy characteristic (magnitude) and scalar seismic moment are investigated. The smallest correlation coefficient was obtained for stress drop.

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

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.

Using machine learning algorithms, the authors here identify slow slip precursors in the Cascadia subduction zone to last for months - which in turn argues for a much better predictability of slow slip rupture.

Acoustic Energy Release During the Laboratory Seismic Cycle: Insights on Laboratory Earthquake Precursors and Prediction

Machine learning can predict the timing and magnitude of laboratory earthquakes using statistics of acoustic emissions. The evolution of acoustic energy is critical for lab earthquake prediction; however, the connections between acoustic energy and fault zone processes leading to failure are poorly understood. Here, we document in detail the temporal evolution of acoustic energy during the laboratory seismic cycle. We report on friction experiments for a range of shearing velocities, normal stresses, and granular particle sizes. Acoustic emission data are recorded continuously throughout shear using broadband piezo-ceramic sensors. The coseismic acoustic energy release scales directly with stress drop and is consistent with concepts of frictional contact mechanics and time-dependent fault healing. Experiments conducted with larger grains (10.5 μm) show that the temporal evolution of acoustic energy scales directly with fault slip rate. In particular, the acoustic energy is low when the fault is locked and increases to a maximum during coseismic failure. Data from traditional slide-hold-slide friction tests confirm that acoustic energy release is closely linked to fault slip rate. Furthermore, variations in the true contact area of fault zone particles play a key role in the generation of acoustic energy. Our data show that acoustic radiation is related primarily to breaking/sliding of frictional contact junctions, which suggests that machine learning-based laboratory earthquake prediction derives from frictional weakening processes that begin very early in the seismic cycle and well before macroscopic failure.

On the Mechanism of Laboratory Earthquake Nucleation Highlighted by Acoustic Emission

Dynamics of granular media is the key to understanding behavior of many natural systems. In this work we concentrate on studying regularities of deformation of a gouge-filled fault. Confined granular layer – model fault – subjected to an external stress may display sudden slip owing to rearrangement of the granular layer. In nature fast slip along a fault results in an earthquake. To understand fault behavior better, we have conducted a comprehensive analysis of acoustic emission (AE) data that accompany stick-slip in granular media. Here we reveal and trace the emergence of two populations of AE. The first one is characterized by a waveform with a harsh onset, while the second one exhibits a gradual amplitude rise and a tremor-like waveform. During a regular stick-slip the statistical properties of the first population remains intact. The second one is very sensitive to alterations of stress conditions, and its scaling parameters correlate with the change of mechanical characteristics of the fault. Probably, AE populations were identified corresponding to two gouge-filled fault subsystems – a load-bearing granular network and an ensemble of relatively unloaded grains in the granular layer. The detected regularities point to a compound self-organization processes in fault zones and suggest that the final stage of earthquake preparation can be revealed in analyzing the scaling characteristics of seismic-acoustic data.

Probing Slow Earthquakes With Deep Learning

Slow earthquakes may trigger failure on neighboring locked faults that are stressed sufficiently to break, and slow slip patterns may evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi-continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory.

Quantitative earthquake-like statistical properties of the flow of soft materials below yield stress

The flow behavior of soft materials below the yield stress can be rich and is not fully understood. Here, we report shear-stress-induced reorganization of three-dimensional solid-like soft materials formed by closely packed nematic domains of surfactant micelles and a repulsive Wigner glass formed by anisotropic clay nano-discs having ionic interactions. The creep response of both the systems below the yield stress results in angular velocity fluctuations of the shearing plate showing large temporal burst-like events that resemble seismic foreshocks-aftershocks data measuring the ground motion during earthquake avalanches. We find that the statistical properties of the quake events inside such a burst map on to the scaling relations for magnitude and frequency distribution of earthquakes, given by Gutenberg-Richter and Omori laws, and follow a power-law distribution of the inter-occurrence waiting time. In situ polarized optical microscopy reveals that during these events the system self-organizes to a much stronger solid-like state.

Damage Evolution and Failure Behavior of Post-Mainshock Damaged Rocks under Aftershock Effects

Rock stability has long been a hot topic during underground energy exploitation, but the failure process of rock materials under earthquake effects is extremely complicated, and the failure mechanism still remains unclear. In order to investigate the fatigue damage and failure behavior of rocks under aftershock effects considering the post-mainshock damage states, a series of laboratory tests were conducted on marble specimens subjected to stepwise cyclic loading. Four levels of peak stress (i.e., 10, 30, 50, and 70 MPa) were applied in the first cycle, to simulate mainshock damage. The results indicate that, with the increase of initial cycle amplitude, mainshock damage has a significant effect on deformation behavior, dissipated energy, P-wave velocity, and AE characteristics of tested specimens during aftershock process. The increasing amplitude of initial cycle enhances irreversible deformation and weakens the resistance to deformation, which accelerates the expansion of specimen volume and results in the reduction of bearing capacity. Furthermore, the increasing amplitude of initial cycle obviously changes the failure morphologies and intensifies the final macro-fracture scale of tested specimens, which are verified by acoustic emission AF-RA value and b-value, respectively.

Train Traffic as a Powerful Noise Source for Monitoring Active Faults With Seismic Interferometry

Laboratory experiments report that detectable seismic velocity changes should occur in the vicinity of fault zones prior to earthquakes. However, operating permanent active seismic sources to monitor natural faults at seismogenic depth is found to be nearly impossible to achieve. We show that seismic noise generated by vehicle traffic, and especially heavy freight trains, can be turned into a powerful repetitive seismic source to continuously probe the Earth's crust at a few kilometers depth. Results of an exploratory seismic experiment in Southern California demonstrate that correlations of train-generated seismic signals allow daily reconstruction of direct P body waves probing the San Jacinto Fault down to 4-km depth. This new approach may facilitate monitoring most of the San Andreas Fault system using the railway and highway network of California.

Experimental and field investigations on seismic response of joints and beddings in rocks

Based on experimental and in-situ tests, the propagation and attenuation rules of seismic wave in the intact and jointed rocks subjected to conventional triaxial loading condition were investigated, especially the influencing effects of joints and beddings on the attenuation. Meanwhile, the frequency-spectrum evolutions during the process of attenuation were analysed in detail. To verify the outcomes obtained from the laboratory, the attenuation characteristics of seismic wave generated by blasting in underground strata were tested, and the attenuation rules by the joints between strata was summarized. Finally, the seismic response of joints and beddings in rocks was revealed. This work put forward some references for early weakening and controlling coal-rock dynamic disasters triggered by seismic wave in coal mines based on the attenuation effect of artificial discontinuity such as joint and bedding.

Continuously Sheared Granular Matter Reproduces in Detail Seismicity Laws

We introduce a shear experiment that quantitatively reproduces the main laws of seismicity. By continuously and slowly shearing a compressed monolayer of disks in a ringlike geometry, our system delivers events of frictional failures with energies following a Gutenberg-Richter law. Moreover, foreshocks and aftershocks are described by Omori laws and interevent times also follow exactly the same distribution as real earthquakes, showing the existence of memory of past events. Other features of real earthquakes qualitatively reproduced in our system are both the existence of a quiescence preceding some main shocks, as well as magnitude correlations linked to large quakes. The key ingredient of the dynamics is the nature of the force network, governing the distribution of frictional thresholds.

Traces Of Laboratory Earthquake Nucleation In The Spectrum Of Ambient Noise

The short-term forecast of earthquakes associated with fault rupture is a challenge in seismology and rock mechanics. The evolution of mechanical characteristics of a local fault segment may be encoded in the ambient noise, thus, converting the ambient noise to an efficient source of information about the fault stress-strain conditions. In laboratory experiments we investigate micro-vibrations of a block-fault system induced by weak external disturbances with the purpose of getting reliable evidence of how the system transits to the metastable state. We show that precursory changes of spectral characteristics of micro-vibrations are observed for the complete spectrum of failure modes. In the course of experiments we systematically change the properties of interface to perform the transition from stick-slip to steady sliding and observe the characteristics of micro-vibrations of the laboratory block-fault system. Detected were systematical alterations of the system natural frequency and those alterations were determined by the evolution of fault stiffness. The detected regularities suggest that the final stage of seismic event preparation can be revealed in analyzing the spectral characteristics of ambient noise. The detection of natural oscillations of a block-fault system can be a new useful tool to monitor active faults in real time.

Slow-slip events in semi-brittle serpentinite fault zones

Slow-slip events are earthquake-like events only with much lower slip rates. While peak coseismic velocities can reach tens of meters per second, slow-slip is on the order of 10^−7±2 m/s and may last for days to weeks. Under the rate-and-state model of fault friction, slow-slip is produced only when the asperity size is commensurate with the critical nucleation size, a function of frictional properties. However, it is unlikely that all subduction zones embody the same frictional properties. In addition to friction, plastic flow of antigorite-rich serpentinite may significantly influence the dynamics of fault slip near the mantle wedge corner. Here, we show that the range of frictional parameters that generate slow slip is widened in the presence of a serpentinized layer along the subduction plate interface. We observe increased stability and damping of fast ruptures in a semi-brittle fault zone governed by both brittle and viscoelastic constitutive response. The rate of viscous serpentinite flow, governed by dislocation creep, is enhanced by high ambient temperatures. When effective viscosity is taken to be dynamic, long-term slow slip events spontaneously emerge. Integration of rheology, thermal effects, and other microphysical processes with rate-and-state friction may yield further insight into the phenomenology of slow slip.

Seismic potential of weak, near-surface faults revealed at plate tectonic slip rates

The near-surface areas of major faults commonly contain weak, phyllosilicate minerals, which, based on laboratory friction measurements, are assumed to creep stably. However, it is now known that shallow faults can experience tens of meters of earthquake slip and also host slow and transient slip events. Laboratory experiments are generally performed at least two orders of magnitude faster than plate tectonic speeds, which are the natural driving conditions for major faults; the absence of experimental data for natural driving rates represents a critical knowledge gap. We use laboratory friction experiments on natural fault zone samples at driving rates of centimeters per year to demonstrate that there is abundant evidence of unstable slip behavior that was not previously predicted. Specifically, weak clay-rich fault samples generate slow slip events (SSEs) and have frictional properties favorable for earthquake rupture. Our work explains growing field observations of shallow SSE and surface-breaking earthquake slip, and predicts that such phenomena should be more widely expected.