Towards GNSS Enhanced Local Tsunami Warning
Abstract
Currently, operational tsunami early warning at NOAA's Tsunami Warning Centers (TWCs) is performed utilizing seismic instrumentation around the globe to provide rapid estimates of the earthquake size and tsunami propagation predictions on a basin-wide scale. These seismic methodologies however tend to rely upon far-field or teleseismic recordings. As a result, earthquake source information and tsunami forecasts for the largest events can be delayed by ~10s of minutes. This can be too late for local communities that would expect tsunami impacts in the first ten minutes after a major megathrust earthquake.
The use of Global Navigation Satellite System (GNSS) displacement data in the near-field is a paradigm shifting technology thanks to its ability to track the motions of large earthquakes without going off-scale. Real-time, high-rate (1 Hz), GNSS networks are currently operational in many countries around the Pacific Rim, including the United States, Canada, Japan, Mexico, New Zealand, Chile, and many others. These networks were originally installed to measure long-term tectonic motions, and over time, were upgraded with higher sample rate receivers and robust telemetry. Because of this, these networks are primed to record the full tectonic cycle and strong ground motions from nearby great earthquakes, often times with greater spatial density than complementary seismic networks. In this presentation we will outline an ongoing effort between NOAA and NASA funded university partners to modernize near-field (local) tsunami forecasting and early warning through the addition of GNSS-derived real-time earthquake source products and the seamless connection to already existing NOAA tsunami modeling codes. Products being integrated include earthquake magnitude using peak ground displacement scaling and earthquake source characteristics, such as focal mechanism and slip distribution, with a centroid moment tensor driven finite fault inversion utilizing coseismic offsets. Here we will describe a strategy for connecting these rapid source models to the operational tsunami forecast system SIFT, to produce a local forecast in the first 3-5 minutes after an earthquake. Finally, we will also discuss a strategy for extensive testing, both online and offline, with historical and synthetic earthquake datasets.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMNH43A..04A
- Keywords:
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- 4313 Extreme events;
- NATURAL HAZARDS;
- 4315 Monitoring;
- forecasting;
- prediction;
- NATURAL HAZARDS;
- 7212 Earthquake ground motions and engineering seismology;
- SEISMOLOGY;
- 7223 Earthquake interaction;
- forecasting;
- and prediction;
- SEISMOLOGY