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Observations of strong ground motion during large earthquakes are generally made with strong-motion accelerometers. These observations have a critical role in early warning systems, seismic engineering, source physics studies, basin and site amplification, and macroseismic intensity estimation. In this manuscript, we present a new observation of strong ground motion made with very high rate (>= 5 Hz) Global Navigation Satellite System (GNSS) derived velocities. We demonstrate that velocity observations recorded on GNSS instruments are consistent with existing ground motion models and macroseismic intensity observations. We find that the ground motion predictions using existing NGA-West2 models match our observed peak ground velocities with a median log total residual of 0.03-0.33 and standard deviation of 0.72-0.79, and are statistically significant following normality testing. We finish by deriving a Ground Motion Model for peak ground velocity from GNSS and find a total residual standard deviation 0.58, which can be improved by ~2% when considering a simple correction for Vs30.
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Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog
Data-driven approaches to identify geophysical signals have proven beneficial in high dimensional environments where model-driven methods fall short. GNSS offers a source of unsaturated ground motion observations that are the data currency of ground motion forecasting and rapid seismic hazard assessment and alerting. However, these GNSS-sourced signals are superposed onto hardware-, location- and time-dependent noise signatures influenced by the Earth’s atmosphere, low-cost or spaceborne oscillators, and complex radio frequency environments. Eschewing heuristic or physics based models for a data-driven approach in this context is a step forward in autonomous signal discrimination. However, the performance of a data-driven approach depends upon substantial representative samples with accurate classifications, and more complex algorithm architectures for deeper scientific insights compound this need. The existing catalogs of high-rate (≥1Hz) GNSS ground motions are relatively limited. In this work, we model and evaluate the probabilistic noise of GNSS velocity measurements over a hemispheric network. We generate stochastic noise time series to augment transferred low-noise strong motion signals from within 70 kilometers of strong events (≥ MW 5.0) from an existing inertial catalog. We leverage known signal and noise information to assess feature extraction strategies and quantify augmentation benefits. We find a classifier model trained on this expanded pseudo-synthetic catalog improves generalization compared to a model trained solely on a real-GNSS velocity catalog, and offers a framework for future enhanced data driven approaches.
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- PAR ID:
- 10485870
- Publisher / Repository:
- Seismica
- Date Published:
- Journal Name:
- Seismica
- Volume:
- 2
- Issue:
- 2
- ISSN:
- 2816-9387
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.26443/seismica.v2i2.978
