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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 With the rise of data volume and computing power, seismological research requires more advanced skills in data processing, numerical methods, and parallel computing. We present the experience of conducting training workshops in various forms of delivery to support the adoption of large-scale high-performance computing (HPC) and cloud computing, advancing seismological research. The seismological foci were on earthquake source parameter estimation in catalogs, forward and adjoint wavefield simulations in 2D and 3D at local, regional, and global scales, earthquake dynamics, ambient noise seismology, and machine learning. This contribution describes the series of workshops delivered as part of research projects, the learning outcomes for participants, and lessons learned by the instructors. Our curriculum was grounded on open and reproducible science, large-scale scientific computing and data mining, and computing infrastructure (access and usage) for HPC and the cloud. We also describe the types of teaching materials that have proven beneficial to the instruction and the sustainability of the program. We propose guidelines to deliver future workshops on these topics. 

Abstract The protracted nature of the 2016-2017 central Italy seismic sequence, with multiple damaging earthquakes spaced over months, presented serious challenges for the duty seismologists and emergency managers as they assimilated the growing sequence to advise the local population. Uncertainty concerning where and when it was safe to occupy vulnerable structures highlighted the need for timely delivery of scientifically based understanding of the evolving hazard and risk. Seismic hazard assessment during complex sequences depends critically on up-to-date earthquake catalogues—i.e., data on locations, magnitudes, and activity of earthquakes—to characterize the ongoing seismicity and fuel earthquake forecasting models. Here we document six earthquake catalogues of this sequence that were developed using a variety of methods. The catalogues possess different levels of resolution and completeness resulting from progressive enhancements in the data availability, detection sensitivity, and hypocentral location accuracy. The catalogues range from real-time to advanced machine-learning procedures and highlight both the promises as well as the challenges of implementing advanced workflows in an operational environment. 

The earthquake catalog includes high-precision hypocenter relocations for 390,334 earthquakes recorded during the 2016-2017 Amatrice (Central Italy)  earthquake sequence. The relative locations were computed by double-difference inversion of a  combination of INGV phase picks and cross-correlation differential  times measured from correlated seismograms with correlation coefficients > 0.7. Planes of normal faults (idx=1-5) are derived from PCA analysis of 2 months of aftershock  locations in the CAT4 catalog following large events. Surfaces of detachment faults (idx=7-10) are derived from mapping out the location of correlated earthquakes.  Citation: Waldhauser, F., Michele, M., Chiaraluce, L., Di Stefano, R., & Schaff, D. P. (2021). Fault planes, fault zone structure and detachment fragmentation resolved with highprecision aftershock locations of the 2016-2017 central Italy sequence. Geophysical Research Letters, 48, e2021GL092918. https://doi.org/10.1029/2021GL092918 

A set of six large catalogues documenting the seismic sequence that occurred in central Italy between 2016 and 2017, characterized by a cascade of four MW5.5–6.5 events. The earthquake catalogues possess different levels of resolution and completeness that result from progressive enhancements in both detection sensitivity and hypocentral location determination. These quality differences reflect the subsequent application of advanced methods. 

Abstract Three devastating earthquakes ofM_W ≥ 5.9 activated a complex system of high‐angle normal, antithetic, and sub‐horizontal detachment faults during the 2016–2017 central Italy seismic sequence. Waveform cross‐correlation based double‐difference location of nearly 400,000 aftershocks illuminate complex, fine‐scale structures of interacting fault zones. The Mt. Vettore–Mt. Bove (VB) normal fault exhibits wide and complex damage zones, including a system of bookshelf faults that intersects the detachment zone. In the Laga domain, a comparatively narrow, shallow dipping segment of the deep Mt. Gorzano fault progressively ruptures through the detachment zone in four subsequentM_W∼ 5.4 events. Reconstructed fault planes show that the detachment zone is fragmented in four sub‐horizontal, partly overlaying shear planes that correlated with the extent of the mainshock ruptures. We find a new, deep reaching seismic barrier that coincides with a bend in the VB fault and may play a role in controlling rupture evolution.