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Tsunamigenic megathrust earthquakes along the Cascadia subduction zone present a major hazard concern. We can better prepare to model the earthquake source in a rapid manner by imbuing fault geometry constraints based on prior knowledge and by evaluating the capabilities of using existing GNSS sensors. Near-field GNSS waveforms have shown promise in providing rapid coarse finite-fault model approximations of the earthquake rupture that can improve tsunami modeling and response time. In this study, we explore the performance of GNSS derived finite-fault inversions and tsunami forecasting predictions in Cascadia that highlights the impact and potential of geodetic techniques and data in operational earthquake and tsunami monitoring. We utilized 1300 Cascadia earthquake simulations (FakeQuakes) that provide realistic (M7.5-9.3) rupture scenarios to assess how feasibly finite-fault models can be obtained in a rapid earthquake early warning and tsunami response context. A series of fault models with rectangular dislocation patches spanning the Cascadia megathrust area is added to the GFAST inversion algorithm to calculate slip for each earthquake scenario. Another method used to constrain the finite-fault geometry is from the GNSS-derived CMT fault plane solution. For the Cascadia region, we show that fault discretization using two rectangular segments approximating the megathrust portion of the subduction zone leads to improvements in modeling magnitude, fault slip, tsunami amplitude, and inundation. In relation to tsunami forecasting capabilities, we compare coastal amplitude predictions spanning from Vancouver Island (Canada) to Northern California (USA). Generally, the coastal amplitudes derived using fault parameters from the CMT solutions show an overestimation bias compared to amplitudes derived from the fixed slab model. We also see improved prediction values of the run-up height and maximum amplitude at 10 tide gauge stations using the fixed slab model as well.
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Tsunami Early Warning From Global Navigation Satellite System Data Using Convolutional Neural Networks
We investigate the potential of using Global Navigation Satellite System (GNSS) observations to directly forecast full tsunami waveforms in real time. We train convolutional neural networks to use less than 9 min of GNSS data to forecast the full tsunami waveforms over 6 hr at select locations, and obtain accurate forecasts on a test data set. Our training and test data consists of synthetic earthquakes and associated GNSS data generated for the Cascadia Subduction Zone using the MudPy software, and corresponding tsunami waveforms in Puget Sound computed using GeoClaw. We use the same suite of synthetic earthquakes and waveforms as in earlier work where tsunami waveforms were used for forecasting, and provide a comparison. We also explore varying the number of GNSS stations, their locations, and their observation durations.
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- Award ID(s):
- 2103713
- PAR ID:
- 10579272
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 49
- Issue:
- 20
- ISSN:
- 0094-8276
- 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/10.1029/2022GL099511
