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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. 

Abstract We introduce BPMF (backprojection and matched filtering)—a complete and fully automated workflow designed for earthquake detection and location, and distributed in a Python package. This workflow enables the creation of comprehensive earthquake catalogs with low magnitudes of completeness using no or little prior knowledge of the study region. BPMF uses the seismic wavefield backprojection method to construct an initial earthquake catalog that is then densified with matched filtering. BPMF integrates recent machine learning tools to complement physics-based techniques, and improve the detection and location of earthquakes. In particular, BPMF offers a flexible framework in which machine learning detectors and backprojection can be harmoniously combined, effectively transforming single-station detectors into multistation detectors. The modularity of BPMF grants users the ability to control the contribution of machine learning tools within the workflow. The computation-intensive tasks (backprojection and matched filtering) are executed with C and CUDA-C routines wrapped in Python code. This leveraging of low-level, fast programming languages and graphic processing unit acceleration enables BPMF to efficiently handle large datasets. Here, we first summarize the methodology and describe the application programming interface. We then illustrate BPMF’s capabilities to characterize microseismicity with a 10 yr long application in the Ridgecrest, California area. Finally, we discuss the workflow’s runtime scaling with numerical resources and its versatility across various tectonic environments and different problems. 

Abstract Repeating earthquakes—sequences of colocated, quasi-periodic earthquakes of similar size—are widespread along California’s San Andreas fault (SAF) system. Catalogs of repeating earthquakes are vital for studying earthquake source processes, fault properties, and improving seismic hazard models. Here, we introduce an unsupervised machine learning-based method for detecting repeating earthquake sequences (RES) to expand existing RES catalogs or to perform initial, exploratory searches. We implement the “SpecUFEx” algorithm (Holtzman et al., 2018) to reduce earthquake spectrograms into low-dimensional, characteristic fingerprints, and apply hierarchical clustering to group similar fingerprints together independent of location, allowing for a global search for potential RES throughout the data set. We then relocate the potential RES and subject them to the same detection criteria as Waldhauser and Schaff (2021). We apply our method to ∼4000 small (ML 0–3.5) earthquakes located on a 10 km long segment of the creeping SAF and double the number of detected RES, allowing for greater spatial coverage of slip-rate estimations at seismogenic depths. Our method is novel in its ability to detect RES independent of initial locations and is complimentary to existing cross-correlation-based methods, leading to more complete RES catalogs and a better understanding of slip rates at depth.