High rate Global Navigation Satellite System (GNSS) deformation time series capture a broad spectrum of earthquake strong motion signals for rapid contributions to hazard warnings and assessment, but experience regular sporadic noise that can be difficult to distinguish from true seismic signals. Previous studies developed methods for automatically detecting these signals but most rely on various external inputs to differentiate true signal from noise. In this study we generated a dataset of high rate GNSS time differenced carrier phase (TDCP) velocity time series concurrent in space and time with expected seismic surface waves from known seismic events. TDCP velocity processing has increased sensitivity relative to traditional geodetic displacement processing without requiring sophisticated corrections. We trained, validated and tested a random forest machine learning classifier. We find our supervised random forest classifier outperforms the existing detection methods in stand-alone mode by combining frequency and time domain features into decision criteria. We optimized the classifier on a balance of sensitivity and false alerting. Within a 100km epicentral radius, the classifier automatically detects 86% of events greater than MW5.0 and 98% of events greater than MW6.0. The classifier model has typical detection latencies seconds behind S-wave arrivals when run in real-time mode on “unseen” events. We conclude the performance of this model provides sufficient confidence to enable these valuable ground motion measurements to run in stand-alone mode for development of edge processing, geodetic infrastructure monitoring and inclusion in operational ground motion observations and models.

This paper presents the results of a campaign covering a week of observations around the July 2, 2019, total Chilean eclipse. The eclipse occurred between 1922–2146 UTC, with complete sun disc obscuration happening at 2038–2040 UTC (1638–1640 LT) over the Andes Lidar Observatory (ALO) at (30.3$^\circ$S,70.7$^\circ$W). Observations were carried out using ALO instrumentation to observe eclipse–induced effects on the mesosphere and lower thermosphere region (MLT) (75–105 km altitude). Several mesosphere-sounding sensors were utilized to collect data before, during, and after the eclipse, including a narrow‐band resonance‐fluorescence 3D winds/temperature Na lidar with daytime observing capability, a meteor radar observing horizontal winds continuously, a multi-color nightglow all-sky camera monitoring the OH(6,2), O$_2$(0,1), O($^1S$), and O($^1D$) emissions, and a mesosphere temperature mapper (MTM) observing the OH(6–2) brightness and rotational temperature. We have also utilized TIMED/SABER temperatures and ionosonde measurements taken at the University of La Serena’s Juan Soldado Observatory. We discuss the effects of the eclipse in the MLT, which can shed light on a sparse set of measurements during this type of event. Our results point out several effects of eclipse–induced changes in the atmosphere below and above but not directly within the MLT. These effects include an unusual fast, bow–shaped gravity wave structure in airglow images, MTM brightness as well as in lidar temperature, strong zonal wind shears above 100 km, the occurrence of a sporadic E layer around 100 km, and finally variations in lidar temperature and density and the presence of a descending sporadic sodium layer near 98 km.