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In Hawaiʻi, tsunamis are often described in orally transmitted legends (moʻolelo). This study examines sedimentary evidence of a possible local submarine landslide-generated tsunami, described in a legend from the south east coast of Maui which originated between the 15th Century CE and the first arrival of Europeans in 1778 CE. Physical evidence for a tsunami, found at the Nu’u Refuge, Maui, is primarily comprised of an extensive coral clast deposit (found 8.5 m above msl and 251 m inland from the shoreline) together with waterworn cobbles which form fracture-embedded wedge clasts in a local basalt escarpment (at up to 8 m above msl). U/Th dating of the coral clasts gives a maximum tsunami deposit age of 1671 CE for the event that may have inspired the local moʻolelo. This depositional sequence is used to characterize the nature of the assumed tsunami in terms of inundation distance, maximum wave runup and minimum flow velocities. A numerical model developed using GeoClaw matches well with the physical evidence. The data and modeling presented here suggest that locallygenerated tsunamis from submarine landslides warrant further research attention as sources of destructive high energy marine inundation events.
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Forecasting tsunami inundation with convolutional neural networks for a potential Cascadia Subduction Zone rupture
Tsunamis in the last two decades have resulted in the loss of life of over 200,000 people and have caused billions of dollars in damage. There is therefore great motivation for the development and improvement of current tsunami warning systems. The work presented here represents advancements made towards the creation of a neural network-based tsunami warning system which can produce fast inundation forecasts with high accuracy. This was done by first improving the waveform resolution and accuracy of Tsunami Squares, an efficient cellular automata approach to wave simulation. It was then used to create a database of precomputed tsunamis in the event of a magnitude 9+ rupture of the Cascadia Subduction Zone, located only ∼100 km off the coast of Oregon, US. Our approach utilized a convolutional neural network which took wave height data from buoys as input and proved successful as maps of maximum inundation could be predicted for the town of Seaside, OR with a median error of ∼0.5 m.
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- PAR ID:
- 10651660
- Publisher / Repository:
- Authorea, Inc.
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript
- Posted Content:
- https://doi.org/10.22541/essoar.167591125.50103833/v1
