Marine observations with optics: Placing sensors on the seafloor is difficult, but a sensor network has huge potential for observing processes occurring both below and above the seafloor. Lindseyet al.measured acoustic vibrations collected by attaching a laser to the Monterey Accelerated Research System's subsea optical fiber during a maintenance period (see the Perspective by Jousset). Acoustic waves were monitored by changes in laser light along the cable. The observations from just a few days allowed mapping of an unknown fault system and detection of several dynamic processes in the water column above.Science, this issue p.1103; see also p.1076

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 a relatively recent development in the use of fiber-optic cable for measurement of ground motion. Discrete fiber-optic sensors, typically using a Bragg diffraction grating, have been in research and development and field testing for more than 15 years with geophysical applications at least 12 years old (Bostick, 2000, and summary in Keul et al., 2005). However, developments in recent years have sought to remove the need for point sensors by using the fiber cable itself as a sensor (Mestayer et al., 2011; Miller et al., 2012).

Our understanding of subsurface processes suffers from a profound observation bias: seismometers are sparse and clustered on continents. A new seismic recording approach, distributed acoustic sensing (DAS), transforms telecommunication fiber‐optic cables into sensor arrays enabling meter‐scale recording over tens of kilometers of linear fiber length. We analyze cataloged earthquake observations from three DAS arrays with different horizontal geometries to demonstrate some possibilities using this technology. In Fairbanks, Alaska, we find that stacking ground motion records along 20 m of fiber yield a waveform that shows a high degree of correlation in amplitude and phase with a colocated inertial seismometer record at 0.8–1.6 Hz. Using an L‐shaped DAS array in Northern California, we record the nearly vertically incident arrival of an earthquake from The Geysers Geothermal Field and estimate its backazimuth and slowness via beamforming for different phases of the seismic wavefield. Lastly, we install a fiber in existing telecommunications conduits below Stanford University and show that little cable‐to‐soil coupling is required for teleseismic P and S phase arrival detection.

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.

Quantitative predictions of natural and induced phenomena in fractured rock is one of the great challenges in the Earth and Energy Sciences with far‐reaching economic and environmental impacts. Fractures occupy a very small volume of a subsurface formation but often dominate fluid flow, solute transport and mechanical deformation behavior. They play a central role in CO₂ sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and hydrocarbon extraction. These applications require predictions of fracture‐dependent quantities of interest such as CO₂ leakage rate, hydrocarbon production, radionuclide plume migration, and seismicity; to be useful, these predictions must account for uncertainty inherent in subsurface systems. Here, we review recent advances in fractured rock research covering field‐ and laboratory‐scale experimentation, numerical simulations, and uncertainty quantification. We discuss how these have greatly improved the fundamental understanding of fractures and one's ability to predict flow and transport in fractured systems. Dedicated field sites provide quantitative measurements of fracture flow that can be used to identify dominant coupled processes and to validate models. Laboratory‐scale experiments fill critical knowledge gaps by providing direct observations and measurements of fracture geometry and flow under controlled conditions that cannot be obtained in the field. Physics‐based simulation of flow and transport provide a bridge in understanding between controlled simple laboratory experiments and the massively complex field‐scale fracture systems. Finally, we review the use of machine learning‐based emulators to rapidly investigate different fracture property scenarios and accelerate physics‐based models by orders of magnitude to enable uncertainty quantification and near real‐time analysis.