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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 Temporal changes in seismic velocity estimated from ambient seismic noise can be utilized to infer subsurface properties at volcanic systems. In this study, we process 7 years of continuous seismic noise at Axial Seamount and use cross‐correlation functions to calculate the relative seismic velocity changes (dv/v) beneath the caldera. We find a long‐term trend of decreasing velocity during rapid inflation, followed by slight increase in velocities as background seismicity increases and inflation rate decreases. Furthermore, we observe small short‐term increases indv/vwhich coincide with short‐term deflation events. Our observations of changes indv/vand their correlation with other geophysical data provide insights into how the top ∼1 km of the crust at Axial Seamount changes in response to subsurface magma movement and capture the transition from a period of rapid reinflation to a period where the caldera wall faults become critically stressed and must rupture to accommodate further inflation. 

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 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. 

Abstract On 5 April 2024, 10:23 a.m. local time, a moment magnitude 4.8 earthquake struck Tewksbury Township, New Jersey, about 65 km west of New York City. Millions of people from Virginia to Maine and beyond felt the ground shaking, resulting in the largest number (>180,000) of U.S. Geological Survey (USGS) “Did You Feel It?” reports of any earthquake. A team deployed by the Geotechnical Extreme Events Reconnaissance Association and the National Institute of Standards and Technology documented structural and nonstructural damage, including substantial damage to a historic masonry building in Lebanon, New Jersey. The USGS National Earthquake Information Center reported a focal depth of about 5 km, consistent with a lack of signal in Interferometric Synthetic Aperture Radar data. The focal mechanism solution is strike slip with a substantial thrust component. Neither mechanism’s nodal plane is parallel to the primary northeast trend of geologic discontinuities and mapped faults in the region, including the Ramapo fault. However, many of the relocated aftershocks, for which locations were augmented by temporary seismic deployments, form a cluster that parallels the general northeast trend of the faults. The aftershocks lie near the Tewksbury fault, north of the Ramapo fault. 

See latest version at: http://dx.doi.org/10.5281/zenodo.4662869 

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