Andrew J. Barbour

Structural variability at the subduction interface imposes stress and strength heterogeneity that affects the behavior of megathrust earthquakes. Characterizing this variability can be challenging because the slab interface is often deep and seismically quiet. Seismic wavefields from intraslab earthquakes can contain high-frequency phase conversions from the megathrust fault that are delayed and distorted by interface topography and velocity heterogeneity. Distributed acoustic sensing (DAS) allows for long-term deployments of dense seismic arrays that can yield many observations of these converted phases at high spatial resolution over large areas. Here, we develop a technique to identify and enhance slab-converted phases in DAS data, and we pick the differential arrival times and relative polarities between the direct and converted phases at short wavelength along the fiber optic cable. We apply this framework to earthquake wavefields measured by a DAS array at the southern end of the Cascadia Subduction Zone, above the locked zone, which suggests kilometer-scale interface roughness and variations in crustal thickness and absolute depth of the Gorda slab. Converted phase wavefields also suggest sharp variability in the presence and position of fluids in the slab and forearc crust.

Edges of sedimentary basins generate complex seismic wavefield effects and localized amplification. Characterizing these effects and incorporating them into hazard models is difficult because tomographic models are often smooth and wavefield observations are often aliased. Distributed acoustic sensing (DAS) transforms telecommunication fibers into ultra‐dense strainmeter arrays, enabling inferences of precise velocity structure, recordings of wavefields at the meter scale over large areas, and measurements of dynamic strain, a quantity with distinct implications for buried infrastructure. With a DAS array in Arcata, California, we resolve a fine‐scale shallow velocity model and compare it to observed wavefields from local earthquakes. We find evidence for multiple basin edges and observe a spatial correlation between these features and locally scattered surface waves that amplify dynamic strain. Two‐dimensional synthetic tests verify that sharp velocity contrasts at basin edges induce these waves and also illustrate how dynamic strain and ground velocity behave differently at these boundaries.

Distributed Acoustic Sensing (DAS) offers a promising approach for earthquake early warning (EEW) in settings where seismic networks are costly to maintain. By repurposing fiber optic cables as dense strainmeter arrays, DAS enables real-time earthquake detection wherever those fibers are accessible. However, poor azimuthal coverage and challenges in estimating magnitude from strain measurements remain key hurdles in applying DAS to earthquake monitoring. Here, we develop a machine learning method to distinguish moderate-to-large (defined here as M ≥ 5.4) earthquakes from smaller ones within the first 4 sec of a strain waveform after a P-wave arrival without determining the earthquake location. Using ensemble decision tree models trained on borehole strainmeter data (3.5 ≤ M ≤ 7.1) and tested on onshore DAS waveforms (including the 2024 M7 Offshore Cape Mendocino earthquake), we find that low-frequency (0.2–0.5 Hz) continuous wavelet transform coefficients are the strongest predictors of magnitude, in addition to strain amplitude. Both DAS and borehole strainmeters effectively capture long-period strain signals, making these results valuable for EEW systems. Our method shows high precision compared to the real-time EEW system, ShakeAlert®, supporting the position that DAS is a viable technology for earthquake monitoring and magnitude classification. Fiber optic cables plus machine learning could aid earthquake early warning by estimating earthquake size within the first few seconds of shaking.

Earthquakes tend to cluster, developing into sequences driven by stress perturbations and transient fault‐zone processes. Depending on the driving process, earthquake sequences show differing behaviors. This variability challenges our ability to observe or distinguish these driving processes in high resolution. Here we systematically identify seismicity bursts throughout southern California using new statistical methods and examine their causes with other independent geophysical observations. Seismicity bursts are defined as statistically significant seismicity rate anomalies. Our approach has the advantage of being data driven, depending on few earthquake occurrence assumptions. We find abundant seismicity bursts across southern California, most frequently occurring along the San Jacinto Fault and in the Salton Sea and Coso geothermal fields. These seismicity bursts are highly compact in space and time, often encompassed by a 5 km radius and have durations less than 10 hr. Many of the seismicity bursts have their largest earthquake near the beginning of the sequence, but the largest earthquake is usually not an obvious mainshock. We utilize a variety of independent geophysical data sets to analyze the characteristics of the seismicity bursts, finding that the seismicity bursts have low b‐values, low spectral stress drops, and varied stress ratios compared to regional seismicity. These differences suggest that seismicity bursts are driven by transient processes acting frequently across fault networks.

The use of fiber-optic sensing systems in seismology has exploded in the past decade. Despite an ever-growing library of ground-breaking studies, questions remain about the potential of fiber-optic sensing technologies as tools for advancing if not revolutionizing earthquake-hazards-related research, monitoring, and early warning systems. A working group convened to explore these topics; we comprehensively examined the application of fiber optics in various aspects of earthquake hazards, encompassing earthquake source processes, crustal imaging, data archiving, and technological challenges. There is great potential for fiber-optic systems to advance earthquake monitoring and understanding, but to fully unlock their capabilities requires continued progress in key areas of research and development, including instrument testing and validation, increased dynamic range for applications focused on larger earthquakes, and continued improvement in subsurface and source imaging methods. A key current stumbling block results from the lack of clear data archiving requirements, and we propose an initial strategy that balances data volume requirements with preserving key data for a broad range of future studies. In addition, we demonstrate the potential for fiber-optic sensing to impact monitoring efforts by documenting the data completeness in a number of long-term experiments. Finally, we outline the features of a instrument testing facility that would enable progress toward reliable and standardized distributed acoustic sensing data. Overcoming these current obstacles would facilitate progress in fiber-optic sensing and unlock its potential application to a broad range of earthquake hazard problems.

Distributed acoustic sensing (DAS), which transforms a fiber optic cable into an array of high frequency strainmeters, has the potential to help us characterize earthquakes with a dense sampling of measurements. While earthquake focal mechanisms are frequently determined using P ‐wave polarities and S / P amplitude ratios with inertial seismometers, the dense sampling of DAS over potentially large portions of the focal sphere can aid our solutions. Here, we consider ∼200 regional earthquakes and thousands of S / P measurements on a DAS cable colocated with a network of inertial seismometers near Arcata, California. We demonstrate the S / P ratio measurements made on DAS are similar to those made on the inertial seismometers and can be used to constrain focal mechanism solutions.

Distributed acoustic sensing (DAS) systems offer a cost-effective way to create large-scale strainmeter arrays for seismological applications using fiber-optic cables. DAS-based strain measurements are known to be influenced by various factors, bringing into question their general reliability for accurate earthquake characterization. A 15-km-long DAS deployment in northern California was operational within 3 days of the 2022 Mw 6.4 Ferndale earthquake and ran continuously throughout the aftershock sequence. We utilize these aftershock data to validate DAS-based strain measurements in two ways. We first test the accuracy of DAS-based magnitude estimates from peak dynamic strains by comparing them with magnitude and attenuation scaling relations derived independently from traditional borehole strainmeter (BSM) data. We demonstrate that DAS-based magnitudes are comparable to BSM-based magnitudes when corrections for variations in site response along the fiber-optic cable are properly made. Magnitude errors are spatially correlated, potentially because of factors such as finite-fault effects (e.g., stress drop) or more complex, unmodeled path attenuation or because of wave propagation effects in heterogeneous media. We then apply more advanced source characterization methodology to the DAS data using a time-domain empirical Green’s function (EGF) deconvolution approach to measure details of the moment rate history. The EGF approach using DAS data depends on careful treatment of distorting factors such as anthropogenic sources of noise and optical phase wrapping but successfully isolates source spectra for moderate-magnitude earthquakes: source spectral ratios obtained from DAS data, broadband seismometer data, and BSM data in the same region show consistent results, revealing differences in directivity and spectral shape among earthquakes. Although further research is needed to refine source-time-function estimation techniques for DAS data, particularly for larger magnitude events, these case studies demonstrate the clear potential of DAS for earthquake source characterization.

Fault structure and rupture physics are deeply intertwined, and observations of this coupling are critical for understanding earthquake behavior. Rupture propagation is observable at fine scales using dense seismic networks. Fiber-optic sensing allows for long-term deployments of ultradense arrays that enable high-resolution measurements of infrequent, large earthquakes. We recorded the 2024 moment magnitude ( M _w ) 7 Mendocino Fault earthquake with a nearby fiber-optic array and imaged its behavior with seismic beamforming. The rupture propagated to the east at subshear velocity; stagnated near the Mendocino Triple Junction, a zone of structural complexity; and subsequently transitioned to supershear velocity. The correlation between source physics and structure shows how lithospheric heterogeneity affects first-order characteristics of earthquake ruptures. Our results also demonstrate the potential for fiber-optic sensing to improve real-time estimation of key parameters for early warning.

Dynamic earthquake triggering is commonly identified through the temporal correlation between increased seismicity rates and global earthquakes that are possible triggering events. However, correlation does not imply causation. False positives may occur when unrelated seismicity rate changes coincidently occur at around the time of candidate triggers. We investigate the expected false positive rate in Southern California with global M ≥ 6 earthquakes as candidate triggers. We compute the false positive rate by applying the statistical tests used by DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487 to synthetic earthquake catalogs with no real dynamic triggering. We find a false positive rate of ∼3.5%–8.5% when realistic earthquake clustering is present, consistent with the 95% confidence typically used in seismology. However, when this false positive rate is applied to the tens of thousands of spatial‐temporal windows in Southern California tested in DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487, thousands of false positives are expected. The expected false positive occurrence is large enough to explain the observed apparent triggering following 70% of large global earthquakes (DeSalvio & Fan, 2023, https://doi.org/10.1029/2023jb026487), without requiring any true dynamic triggering. Aside from the known triggering from the nearby El Mayor‐Cucapah, Mexico, earthquake, the spatial and temporal characteristics of the reported triggering are indistinguishable from random false positives. This implies that best practice for dynamic triggering studies that depend on temporal correlation is to estimate the false positive rate and investigate whether the observed apparent triggering is distinguishable from the correlations that may occur by chance.

The southernmost portion of the Cascadia subduction zone in northern California produces high rates of moderate and large earthquakes owing to subduction of the Gorda slab and the deformation associated with the Mendocino Triple Junction. Distributed acoustic sensing (DAS) is rapidly advancing as a method for detecting earthquakes and imaging crustal structure. We have begun a long-term DAS monitoring experiment on buried telecom fiber in Arcata, California, to increase the available recordings of moderate-to-large earthquakes as well as image seismogenic structures. We have recorded over a year’s worth of data, including most aftershocks of the 2022 Mw 6.4 Ferndale earthquake, though not the mainshock itself. The dataset includes numerous magnitude 3.5 and larger earthquakes, including the 1 January 2023 Mw 5.4 Rio Dell earthquake. Here we present initial results comparing an earthquake detection algorithm, run in real time on the processing unit of the interrogator system, with both the ShakeAlert® earthquake early warning system and a postprocessed earthquake catalog developed with deep-learning phase-picker algorithms. The rapid onboard processing of the detector demonstrates the potential utility of DAS-based edge computing for earthquake early warning. We also verify the quality of the strain waveforms both in terms of peak amplitudes and waveform similarity using about five months of nodal seismometer data. These instruments were deployed roughly every 300 m along the ∼15-km-long cable and validate large variations in peak strain over short distances that are seen in the DAS data. All data from time windows surrounding both the local and teleseismic earthquakes are publicly available, which will improve our understanding of both the performance of DAS systems in moderate earthquakes and earthquake hazards associated with the Gorda subduction zone.