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Abstract Distributed acoustic sensing (DAS) on submarine fiber-optic cables is providing new observational insights into solid Earth processes and ocean dynamics. However, the availability of offshore dark fibers for long-term deployment remains limited. Simultaneous telecommunication and DAS operating at different wavelengths in the same fiber, termed optical multiplexing, offers one solution. In May 2024, we collected a four-day DAS dataset utilizing an L-band DAS interrogator and multiplexing on the submarine cables of the Ocean Observatory Initiative’s Regional Cabled Array offshore central Oregon. Our findings show that multiplexed DAS has no impact on communications and is unaffected by network traffic. Moreover, the quality of DAS data collected via multiplexing matches that of data obtained from dark fiber. With a machine-learning event detection workflow, we detect 31 T waves and the S wave of one regional earthquake, demonstrating the feasibility of continuous earthquake monitoring using the multiplexed offshore DAS. We also examine ocean waves and ocean-generated seismic noise. We note high-frequency seismic noise modulated by low-frequency ocean swell and hypothesize about its origins. The complete dataset is freely available. 

Abstract Seismicity at active volcanoes provides crucial constraints on the dynamics of magma systems and complex fault activation processes preceding and during an eruption. We characterize time‐dependent spectral features of volcanic earthquakes at Axial Seamount with unsupervised machine learning (ML) methods, revealing mixed frequency signals that rapidly increase in number about 15 hr before eruption onset. The events migrate along pre‐existing fissures, suggesting that they represent brittle crack opening driven by influx of magma or volatiles. These results demonstrate the power of unsupervised ML algorithms to characterize subtle changes in magmatic processes associated with eruption preparation, offering new possibilities for forecasting Axial's anticipated next eruption. This analysis is generalizable and can be employed to identify similar precursory signals at other active volcanoes. 

Abstract Axial Seamount, an extensively instrumented submarine volcano, lies at the intersection of the Cobb–Eickelberg hot spot and the Juan de Fuca ridge. Since late 2014, the Ocean Observatories Initiative (OOI) has operated a seven-station cabled ocean bottom seismometer (OBS) array that captured Axial’s last eruption in April 2015. This network streams data in real-time, facilitating seismic monitoring and analysis for volcanic unrest detection and eruption forecasting. In this study, we introduce a machine learning (ML)-based real-time seismic monitoring framework for Axial Seamount. Combining both supervised and unsupervised ML and double-difference techniques, we constructed a comprehensive, high-resolution earthquake catalog while effectively discriminating between various seismic and acoustic events. These events include earthquakes generated by different physical processes, acoustic signals of lava–water interaction, and oceanic sources such as whale calls. We first built a labeled ML-based earthquake catalog that extends from November 2014 to the end of 2021 and then implemented real-time monitoring and seismic analysis starting in 2022. With the rapid determination of high-resolution earthquake locations and the capability to track potential precursory signals and coeruption indicators of magma outflow, this system may improve eruption forecasting by providing short-term constraints on Axial’s next eruption. Furthermore, our work demonstrates an effective application that integrates unsupervised learning for signal discrimination in real-time operation, which could be adapted to other regions for volcanic unrest detection and enhanced eruption forecasting.