Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog
Author:
Dittmann TimothyORCID, Morton Y. JadeORCID, Crowell BrendanORCID, Melgar Diego, DeGrande JensenORCID, Mencin DavidORCID
Abstract
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.
Publisher
McGill University Library and Archives
Reference70 articles.
1. Allen, R. M., & Ziv, A. (2011). Application of real-time GPS to earthquake early warning. Geophysical Research Letters, 38(16). https://doi.org/10.1029/2011gl047947 2. Ancheta, T. D., Darragh, R. B., Stewart, J. P., Seyhan, E., Silva, W. J., Chiou, B. S.-J., Wooddell, K. E., Graves, R. W., Kottke, A. R., Boore, D. M., Kishida, T., & Donahue, J. L. (2014). NGA-West2 Database. Earthquake Spectra, 30(3), 989–1005. https://doi.org/10.1193/070913eqs197m 3. Avallone, A., Marzario, M., Cirella, A., Piatanesi, A., Rovelli, A., Alessandro, C. D., D’Anastasio, E., D’Agostino, N., Giuliani, R., & Mattone, M. (2011). Very high rate (10 Hz) GPS seismology for moderate‐magnitude earthquakes: The case of the Mw 6.3 L’Aquila (central Italy) event. Journal of Geophysical Research, 116(B2). https://doi.org/10.1029/2010jb007834 4. Benedetti, E., Branzanti, M., Biagi, L., Colosimo, G., Mazzoni, A., & Crespi, M. (2014). Global Navigation Satellite Systems Seismology for the 2012 Mw 6.1 Emilia Earthquake: Exploiting the VADASE Algorithm. Seismological Research Letters, 85(3), 649–656. https://doi.org/10.1785/0220130094 5. Bergen, K. J., Johnson, P. A., de Hoop, M. V., & Beroza, G. C. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science, 363(6433). https://doi.org/10.1126/science.aau0323
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