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.

Monitoring Seismic Velocity Changes Across the San Jacinto Fault Using Train-Generated Seismic Tremors.

Microseismic noise has been used for seismic velocity monitoring. However, such signals are dominated by low-frequency surface waves that are not ideal for detecting changes associated with small tectonic processes. Here we show that it is possible to extract stable, high-frequency body waves using seismic tremors generated by freight trains. Such body waves allow us to focus on small velocity perturbations in the crust with high spatial resolution. We report on 10 years of seismic velocity temporal changes at the San Jacinto Fault. We observe and map a two-month-long episode of velocity changes with complex spatial distribution and interpret the velocity perturbation as produced by a previously undocumented slow-slip event. We verify the hypothesis through numerical simulations and locate this event along a fault segment believed to be locked. Such a slow-slip event stresses its surroundings and may trigger a major earthquake on a fault section approaching failure.

Seismic Stereometry Reveals Preparatory Behavior and Source Kinematics of Intermediate-Size Earthquakes.

Although moderate-size earthquakes are poorly studied by lack of near-fault observations, they can provide key information about larger damaging earthquakes. Here we propose a new approach, inspired by double-difference relocation, that uses high-coherency waveforms recorded at neighboring sensors, to study the preparation phase and dynamics of moderate-size earthquakes. We validate this technique by analyzing the 2016, M w 5.2 Borrego Springs earthquake in Southern California and find consistent rupture velocities of 2 km/s highlighting two main rupture asperities. The analysis of the 2019, Ml5.2 Le Teil earthquake in France reveals slow nucleation at depth that migrates to the surface and propagates northward with a velocity of ∼2.8 km/s, highlighting two main rupture events also imaged by InSAR. By providing unprecedented resolution in our observation of the rupture dynamics, this approach will be useful in better understanding the preparation phase and rupture of both tectonic and induced earthquakes.

Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano.

Volcanic tremor is key to our understanding of active magmatic systems, but due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August-October 2015 eruption as well as the closing of the eruptive vent during the September-November 2018 eruption. The machine learning workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptions.

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.

Earthquake dynamics. Mapping pressurized volcanic fluids from induced crustal seismic velocity drops.

Volcanic eruptions are caused by the release of pressure that has accumulated due to hot volcanic fluids at depth. Here, we show that the extent of the regions affected by pressurized fluids can be imaged through the measurement of their response to transient stress perturbations. We used records of seismic noise from the Japanese Hi-net seismic network to measure the crustal seismic velocity changes below volcanic regions caused by the 2011 moment magnitude (M(w)) 9.0 Tohoku-Oki earthquake. We interpret coseismic crustal seismic velocity reductions as related to the mechanical weakening of the pressurized crust by the dynamic stress associated with the seismic waves. We suggest, therefore, that mapping seismic velocity susceptibility to dynamic stress perturbations can be used for the imaging and characterization of volcanic systems.

Postseismic relaxation along the San Andreas fault at Parkfield from continuous seismological observations.

Seismic velocity changes and nonvolcanic tremor activity in the Parkfield area in California reveal that large earthquakes induce long-term perturbations of crustal properties in the San Andreas fault zone. The 2003 San Simeon and 2004 Parkfield earthquakes both reduced seismic velocities that were measured from correlations of the ambient seismic noise and induced an increased nonvolcanic tremor activity along the San Andreas fault. After the Parkfield earthquake, velocity reduction and nonvolcanic tremor activity remained elevated for more than 3 years and decayed over time, similarly to afterslip derived from GPS (Global Positioning System) measurements. These observations suggest that the seismic velocity changes are related to co-seismic damage in the shallow layers and to deep co-seismic stress change and postseismic stress relaxation within the San Andreas fault zone.