Probabilistic assessment of landslide tsunami hazard for the northern Gulf of Mexico Academic Article uri icon

abstract

  • © 2016. American Geophysical Union. All Rights Reserved. The devastating consequences of recent tsunamis affecting Indonesia and Japan have prompted a scientific response to better assess unexpected tsunami hazards. Although much uncertainty exists regarding the recurrence of large-scale tsunami events in the Gulf of Mexico (GoM), geological evidence indicates that a tsunami is possible and would most likely come from a submarine landslide triggered by an earthquake. This study customizes for the GoM a first-order probabilistic landslide tsunami hazard assessment. Monte Carlo Simulation (MCS) is employed to determine landslide configurations based on distributions obtained from observational submarine mass failure (SMF) data. Our MCS approach incorporates a Cholesky decomposition method for correlated landslide size parameters to capture correlations seen in the data as well as uncertainty inherent in these events. Slope stability analyses are performed using landslide and sediment properties and regional seismic loading to determine landslide configurations which fail and produce a tsunami. The probability of each tsunamigenic failure is calculated based on the joint probability of slope failure and probability of the triggering earthquake. We are thus able to estimate sizes and return periods for probabilistic maximum credible landslide scenarios. We find that the Cholesky decomposition approach generates landslide parameter distributions that retain the trends seen in observational data, improving the statistical validity and relevancy of the MCS technique in the context of landslide tsunami hazard assessment. Estimated return periods suggest that probabilistic maximum credible SMF events in the north and northwest GoM have a recurrence of 5000-8000 years, in agreement with age dates of observed deposits.

altmetric score

  • 0.75

author list (cited authors)

  • Pampell‐Manis, A., Horrillo, J., Shigihara, Y., & Parambath, L.

citation count

  • 12

publication date

  • January 2016