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ABSTRACT The foundation of earthquake monitoring is the ability to rapidly detect, locate, and estimate the size of seismic sources. Earthquake magnitudes are particularly difficult to rapidly characterize because magnitude types are only applicable to specific magnitude ranges, and location errors propagate to substantial magnitude errors. We developed a method for rapid estimation of single-station earthquake magnitudes using raw three-component P waveforms observed at local to teleseismic distances, independent of prior size or location information. We used the MagNet regression model architecture (Mousavi and Beroza, 2020b), which combines convolutional and recurrent neural networks. We trained our model using ∼2.4 million P-phase arrivals labeled by the authoritative magnitude assigned by the U.S. Geological Survey. We tested input data parameters (e.g., window length) that could affect the performance of our model in near-real-time monitoring applications. At the longest waveform window length of 114 s, our model (Artificial Intelligence Magnitude [AIMag]) is accurate (median estimated magnitude within ±0.5 magnitude units from catalog magnitude) between M 2.3 and 7.6. However, magnitudes above M ∼7 are more underestimated as true magnitude increases. As the windows are shortened down to 1 s, the point at which higher magnitudes begin to be underestimated moves toward lower magnitudes, and the degree of underestimation increases. The over and underestimation of magnitudes for the smallest and largest earthquakes, respectively, are potentially related to the limited number of events in these ranges within the training data, as well as magnitude saturation effects related to not capturing the full source time function of large earthquakes. Importantly, AIMag can determine earthquake magnitudes with individual stations’ waveforms without instrument response correction or knowledge of an earthquake’s source-station distance. This work may enable monitoring agencies to more rapidly recognize large, potentially tsunamigenic global earthquakes from few stations, allowing for faster event processing and reporting. This is critical for timely warnings for seismic-related hazards. 

Abstract Ocean swell interacting with Antarctic ice shelves produces sustained (approximately, 2×106 cycles per year) gravity-elastic perturbations with deformation amplitudes near the ice front as large as tens to hundreds of nanostrain. This process is the most energetically excited during the austral summer, when sea ice-induced swell attenuation is at a minimum. A 2014–2017 deployment of broadband seismographs on the Ross Ice shelf, which included three stations sited, approximately, 2 km from the ice front, reveals prolific swell-associated triggering of discrete near-ice-front (magnitude≲0) seismic subevents, for which we identify three generic types. During some strong swell episodes, subevent timing becomes sufficiently phase-locked with swell excitation, to create prominent harmonic features in spectra calculated across sufficiently lengthy time windows via a Dirac comb effect, for which we articulate a theoretical development for randomized interevent times. These events are observable at near-front stations, have dominant frequency content between 0.5 and 20 Hz, and, in many cases, show highly repetitive waveforms. Matched filtering detection and analysis shows that events occur at a low-background rate during all swell states, but become particularly strongly excited during large amplitude swell at rates of up to many thousands per day. The superimposed elastic energy from swell-triggered sources illuminates the shelf interior as extensional (elastic plate) Lamb waves that are observable more than 100 km from the ice edge. Seismic swarms show threshold excitation and hysteresis with respect to rising and falling swell excitation. This behavior is consistent with repeated seismogenic fracture excitation and growth within a near-ice-front damage zone, encompassing fracture features seen in satellite imagery. A much smaller population of distinctly larger near-front seismic events, previously noted to be weakly associated with extended periods of swell perturbation, likely indicate calving or other larger-scale ice failures near the shelf front.