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Ambient-noise-based seismic monitoring of the near surface often has limited spatiotemporal resolutions because dense seismic arrays are rarely sufficiently affordable for such applications. In recent years, however, distributed acoustic sensing (DAS) techniques have emerged to transform telecommunication fiber-optic cables into dense seismic arrays that are cost effective. With DAS enabling both high sensor counts (“large N”) and long-term operations (“large T”), time-lapse imaging of shear-wave velocity ( V S ) structures is now possible by combining ambient noise interferometry and multichannel analysis of surface waves (MASW). Here we report the first end-to-end study of time-lapse V S imaging that uses traffic noise continuously recorded on linear DAS arrays over a three-week period. Our results illustrate that for the top 20 meters the V S models that is well constrained by the data, we obtain time-lapse repeatability of about 2% in the model domain—a threshold that is low enough for observing subtle near-surface changes such as water content variations and permafrost alteration. This study demonstrates the efficacy of near-surface seismic monitoring using DAS-recorded ambient noise.

Understanding physical processes prior to and during volcanic eruptions has improved significantly in recent years. However, uncertainties about subsurface structures distorting observed signals and undetected processes within the volcano prevent volcanologists to infer subtle triggering mechanisms of volcanic phenomena. Here, we demonstrate that distributed acoustic sensing (DAS) with optical fibres allows us to identify volcanic events remotely and image hidden near-surface volcanic structural features. We detect and characterize strain signals associated with explosions and locate their origin using a 2D-template matching between picked and theoretical wave arrival times. We find evidence for non-linear grain interactions in a scoria layer of spatially variable thickness. We demonstrate that wavefield separation allows us to incrementally investigate the ground response to various excitation mechanisms. We identify very small volcanic events, which we relate to fluid migration and degassing. Those results provide the basis for improved volcano monitoring and hazard assessment using DAS. Fibre Optic Distributed Acoustic Sensing on Mount Etna provides new opportunities for monitoring volcanic processes and demonstrates nonlinear interaction of infrasound wave with scoria layer, mapping its thickness and illuminating hidden structures.

Laboratory experiments and geodynamic simulations demonstrate that poloidal- and toroidal-mode mantle flows develop around subduction zones. Here, we use a new 3-D azimuthal anisotropy model constructed by full waveform inversion, to infer deep subduction-induced mantle flows underneath Middle America. At depths shallower than 150 km, poloidal-mode flow is perpendicular to the trajectory of the Middle American Trench. From 300 to 450 km depth, return flows surround the edges of the Rivera and Atlantic slabs, while escape flows are inferred through slab windows beneath Panama and central Mexico. Furthermore, at 700 km depth, the study region is dominated by the Farallon anomaly, with fast axes perpendicular to its strike, suggesting the development of lattice-preferred orientations by substantial stress. These observations provide depth-dependent seismic anisotropy for future mantle flow simulations, and call for further investigations about the deformation mechanisms and elasticity of minerals in the transition zone and uppermost lower mantle. The motions of subducted slabs are expected to drive mantle flow around slab edges, however, evidence of deep mantle flow has so far remained elusive. Here, the authors present a Full Waveform Inversion 3-D anisotropy model which allows them to infer deep subduction-induced mantle flows underneath the Mid-Americas and the Caribbean.

Fault-zone fluids control effective normal stress and fault strength. While most earthquake models assume a fixed pore fluid pressure distribution, geologists have documented fault valving behavior, that is, cyclic changes in pressure and unsteady fluid migration along faults. Here we quantify fault valving through 2-D antiplane shear simulations of earthquake sequences on a strike-slip fault with rate-and-state friction, upward Darcy flow along a permeable fault zone, and permeability evolution. Fluid overpressure develops during the interseismic period, when healing/sealing reduces fault permeability, and is released after earthquakes enhance permeability. Coupling between fluid flow, permeability and pressure evolution, and slip produces fluid-driven aseismic slip near the base of the seismogenic zone and earthquake swarms within the seismogenic zone, as ascending fluids pressurize and weaken the fault. This model might explain observations of late interseismic fault unlocking, slow slip and creep transients, swarm seismicity, and rapid pressure/stress transmission in induced seismicity sequences. Coupling between fault zone fluid flow, permeability evolution, and elastic stress transfer produces fault valving and fluid-driven aseismic slip and pore pressure pulses. This model might explain late interseismic fault unlocking, slow slip, and rapid pressure transmission in induced seismicity.

We present one of the first case studies demonstrating the use of distributed acoustic sensing deployed on regional unlit fiber-optic telecommunication infrastructure (dark fiber) for broadband seismic monitoring of both near-surface soil properties and earthquake seismology. We recorded 7 months of passive seismic data on a 27 km section of dark fiber stretching from West Sacramento, CA to Woodland, CA, densely sampled at 2 m spacing. This dataset was processed to extract surface wave velocity information using ambient noise interferometry techniques; the resulting V S profiles were used to map both shallow structural profiles and groundwater depth, thus demonstrating that basin-scale variations in hydrological state could be resolved using this technique. The same array was utilized for detection of regional and teleseismic earthquakes and evaluated for long period response using records from the M8.1 Chiapas, Mexico 2017, Sep 8th event. The combination of these two sets of observations conclusively demonstrates that regionally extensive fiber-optic networks can effectively be utilized for a host of geoscience observation tasks at a combination of scale and resolution previously inaccessible.

Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring. In this study, the authors develop a semi-supervised approach to train a deep learning model, PhaseNet-DAS, for identifying seismic phases in Distributed Acoustic Sensing (DAS) data, which enables detecting and locating earthquakes using fiber-optic networks.

The current lithospheric root of the South China Block has been partly removed, yet what mechanisms modified the lithospheric structure remain highly controversial. Here we use a new joint seismic inversion algorithm to image tabular high-velocity anomalies at depths of ~90–150 km in the asthenosphere beneath the convergent belt between the Yangtze and Cathaysia blocks that remain weakly connected with the stable Yangtze lithosphere. Based on obtained seismic images and available geochemical data, we interpret these detached fast anomalies as partially destabilized lower lithosphere that initially delaminated at 180–170 Ma and has relaminated to their original position after warming up in the mantle by now. We conclude that delamination is the most plausible mechanism for the lithospheric modification and the formation of a Mesozoic Basin and Range-style magmatic province in South China by triggering adiabatic upwelling of the asthenosphere and consequent lithospheric extension and extensive melting of the overlying crust. Lithospheric delamination is seismically imaged in the asthenosphere and is responsible for the lithospheric modification and the formation of a Mesozoic Basin and Range-style magmatic province in South China by joint analysis of geochemical data. ̈

Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. CRED uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on individual stations. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. We also apply the model to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 800 microearthquakes as small as −1.3 ML induced during hydraulic fracturing far away than the training region. We compare the performance of the model with the STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise for lowering the detection threshold while minimizing false positive detection rates.

Geological heterogeneity is abundant in crustal fault zones; however, its role in controlling the mechanical behaviour of faults is poorly constrained. Here, we present laboratory friction experiments on laterally heterogeneous faults, with patches of strong, rate-weakening quartz gouge and weak, rate-strengthening clay gouge. The experiments show that the heterogeneity leads to a significant reduction in strength and frictional stability in comparison to compositionally identical faults with homogeneously mixed gouges. We identify a combination of weakening effects, including smearing of the weak clay; differential compaction of the two gouges redistributing normal stress; and shear localization producing stress concentrations in the strong quartz patches. The results demonstrate that geological heterogeneity and its evolution can have pronounced effects on fault strength and stability and, by extension, on the occurrence of slow-slip transients versus earthquake ruptures and the characteristics of the resulting events, and should be further studied in lab experiments and earthquake source modelling. This paper shows that faults comprised of heterogeneously distributed materials, as is typical for tectonic faults in nature, are weaker and more unstable than equivalent faults where the materials are homogeneously mixed together.

Silicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si–O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si–O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning. Understanding the silicon-oxygen system is crucial for various applications. Here, the authors present an interatomic potential covering a wide range of the Si-O configurational space and showcase applications to silica and Si-SiO2 interfaces.