Globally, instrumentally based assessments of tsunamigenic potential of subduction zones have underestimated the magnitude and frequency of great events because of their short time record. Historical and sediment records of large earthquakes and tsunamis have expanded the temporal data and estimated size of these events. Instrumental records suggests that the Mexican Subduction earthquakes produce relatively small tsunamis, however historical records and now geologic evidence suggest that great earthquakes and tsunamis have whipped the Pacific coast of Mexico in the past. The sediment marks of centuries old-tsunamis validate historical records and indicate that large tsunamigenic earthquakes have shaken the Guerrero-Oaxaca region in southern Mexico and had an impact on a bigger stretch of the coast than previously suspected. We present the first geologic evidence of great tsunamis near the trench of a subduction zone previously underestimated as potential source for great earthquakes and tsunamis. Two sandy tsunami deposits extend over 1.5 km inland of the coast. The youngest tsunami deposit is associated with the 1787 great earthquake, M 8.6, producing a giant tsunami that poured over the coast flooding 500 km alongshore the Mexican Pacific coast and up to 6 km inland. The oldest event from a less historically documented event occurred in 1537.more »
This content will become publicly available on December 20, 2023
Real‐time tsunami prediction is necessary for tsunami forecasting. Although tsunami forecasting based on a precomputed tsunami simulation database is fast, it is difficult to respond to earthquakes that are not in the database. As the computation speed increases, various alternatives based on physics‐based models have been proposed. However, physics‐based models still require several minutes to simulate tsunamis and can have numerical stability issues that potentially make them unreliable for use in forecasting—particularly in the case of near‐field tsunamis. This paper presents a data‐driven model called the tsunami runup response function for finite faults (TRRF‐FF) model that can predict alongshore near‐field tsunami runup distribution from heterogeneous earthquake slip distribution in less than a second. Once the TRRF‐FF model is trained and calibrated based on a discrete set of tsunami simulations, the TRRF‐FF model can predict alongshore tsunami runup distribution from any combination of finite fault parameters. The TRRF‐FF model treats the leading‐order contribution and the residual part of the alongshore tsunami runup distribution separately. The interaction between finite faults is modeled based on the leading‐order alongshore tsunami runup distribution. We validated the TRRF‐FF modeling approach with more than 200 synthetic tsunami scenarios in eastern Japan. We further explored the performance more »
- Publication Date:
- NSF-PAR ID:
- 10392942
- Journal Name:
- Journal of Geophysical Research: Oceans
- Volume:
- 128
- Issue:
- 1
- ISSN:
- 2169-9275
- Publisher:
- DOI PREFIX: 10.1029
- Sponsoring Org:
- National Science Foundation
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Abstract -
Abstract The Kalapana, Hawaii,
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