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.

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We present a new automated earthquake detection and location method based on beamforming (or back projection) and template matching and apply it to study the seismicity of the Southwestern Alps. We use beamforming with prior knowledge of the 3-D variations of seismic velocities as a first detection run to search for earthquakes that are used as templates in a subsequent matched-filter search. Template matching allows us to detect low signal-to-noise ratio events and thus to obtain a high spatiotemporal resolution of the seismicity in the Southwestern Alps. We describe how we address the problem of false positives in energy-based earthquake detection with supervised machine learning and how to best leverage template matching to iteratively refine the templates and the detection. We detected 18,754 earthquakes over 1 year (our catalog is available online) and observed temporal clustering of the earthquake occurrence in several regions. This statistical study of the collective behavior of earthquakes provides insights into the mechanisms of earthquake occurrence. Based on our observations, we infer the mechanisms responsible for the seismic activity in three regions of interest: the Ubaye valley, the Briançonnais, and the Dora Maira massif. Our conclusions point to the importance of fault interactions to explain the earthquake occurrence in the Briançonnais and the Dora Maira massif, whereas fluids seem to be the major driving mechanism in the Ubaye valley.

Continuous seismograms contain a wealth of information with a large variety of signals with different origin. Identifying these signals is a crucial step in understanding physical geological objects. We propose a strategy to identify classes of signals in continuous single‐station seismograms in an unsupervised fashion. Our strategy relies on extracting meaningful waveform features based on a deep scattering network combined with an independent component analysis. Based on the extracted features, agglomerative clustering then groups these waveforms in a hierarchical fashion and reveals the process of clustering in a dendrogram. We use the dendrogram to explore the seismic data and identify different classes of signals. To test our strategy, we investigate a two‐day‐long seismogram collected in the vicinity of the North Anatolian Fault, Turkey. We analyze the automatically inferred clusters' occurrence rate, spectral characteristics, cluster size, and waveform and envelope characteristics. At a low level in the cluster hierarchy, we obtain three clusters related to anthropogenic and ambient seismic noise and one cluster related to earthquake activity. At a high level in the cluster hierarchy, we identify a seismic burst that includes around 200 events with similar waveforms and high‐frequent signals with correlating envelopes and an anthropogenic origin. The application shows that the cluster hierarchy helps to identify particular families of signals and to extract subclusters for further analysis. This is valuable when certain types of signals, such as earthquakes, are under‐represented in the data. The proposed method may also successfully discover new types of signals since it is entirely data‐driven.

Foreshocks in the form of microseismicity are among the most powerful tools to study the physical processes that occur before main earthquakes. However, their detection and precise characterization is still sparse, especially for small-to-moderate-size earthquakes (Mw<6). We present here a detailed foreshock analysis for the 7 November 2019, Balsorano, Italy, normal fault earthquake (Mw 4.4). To improve the detection of the microseismicity before and after the mainshock, we use six three-component broadband receivers at distances of less than 75 km from the targeted seismicity, through template matching. To improve the understanding of the physical mechanism(s) behind the earthquake initiation process, as well as other accompanying phenomena, we also detail the spatiotemporal evolution of the sequence associated with this medium-sized earthquake, using waveform clustering and hypocenter relocation. Clear differences between foreshocks and aftershocks are revealed by this analysis. Moreover, five distinct spatiotemporal patterns associated with the different seismic activities are revealed. The observed spatiotemporal behavior shown by the foreshocks highlights a complex initiation process, which apparently starts on an adjacent unmapped antithetic fault. Finally, the aftershock activity comprises four different clusters with distinct spatiotemporal patterns, which suggests that the different clusters in this sequence have distinct triggering mechanisms.