We introduce a modular software framework designed to integrate distributed acoustic sensing (DAS) data into operational earthquake monitoring systems. Building on the infrastructure of the Advanced National Seismic System (ANSS) and the Southern California Seismic Network, which employs the ANSS Quake Monitoring Software (AQMS), our solution supports real-time DAS waveform streaming and machine-learning-based travel-time picking to leverage the dense spatial sampling of DAS arrays. To enable seamless compatibility with the AQMS, our approach uses standardized seismic data formats to incorporate predetermined DAS channels. We demonstrate the integration of data from a 100-km-long DAS array deployed in Ridgecrest, California, and provide a detailed description of the software components and deployment strategy. This work represents a step toward incorporating DAS into routine seismic monitoring and opens new possibilities for real-time hazard assessment using fiber-optic networks.

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.

High-temperature superconductor (HTS) magnet systems, especially those designed for fusion reactors, require effective and reliable monitoring to avoid damaging anomalies. In tokamaks, some of the magnetic coils are time-dependent, which causes strain and large inductive voltages within the magnet, rendering detection of incipient quench challenging. Ionizing radiation can also create material defects and lead to non-uniform degradation of conductors. The resulting decrease in critical current uniformity across the magnet, along with manufacturing defects, such as failure of structural materials or cooling systems, can all potentially initiate a quench. HTS magnets have a lower normal zone propagation velocity than low-temperature superconductors, and this causes normal zones to be localized, increasing the risk of permanent damage. Fiber optic sensors have several qualities that are essential in fusion systems. Unlike traditional voltage-based sensors, fiber optic cables are immune to the large electromagnetic fields present. This study presents and validates a fiber optic interrogation technique for monitoring magnetic confinement fusion and other high-temperature superconducting magnet systems. Coherent-phase optical time domain reflectometry (OTDR) allows for the high sampling rates (tens of kHz) necessary to quickly detect and mitigate quench events over the long distances required to monitor fusion magnet systems. This technique was demonstrated to successfully detect localized thermal transients at cryogenic temperatures as low as 6 K. These outcomes were also demonstrated using fibers embedded in HTS magnet coils at 77 K, verifying the potential for this interrogation technique’s use for failure detection in HTS coils.