A Monte Carlo approach for estimating tsunami hazard from submarine mass failure along the U.S. East coast Chris Baxter 1 , Stephan Grilli 1 , and Teresa Krause 1 1 Department of Ocean Engineering, University of Rhode Island, Narragansett, Rhode Island, USA Abstract This work is being conducted as part of the development of tsunami inundation maps for the U.S. East Coast (USEC), as mandated by the National Tsunami Hazard Mitigation Program (NTHMP). Along the USEC, which borders the Atlantic Ocean Basin, tsunami hazard may result from large distant co-seismic sources (e.g., in the Puerto Rico Trench or the Azores convergence zone) or volcanic flank collapse sources (e.g., in the Canary Islands). More importantly, however, tsunami hazard may result from Submarine Mass Failures (SMFs) occurring along the nearby continental shelf break and slope (e.g., 1929 Grand Bank). Indeed, potentially large tsunamigenic SMFs can be triggered by moderate seismic activity, such as could occur along the USEC, and cause large local tsunamis. While many past SMFs have been identified along the USEC and described in various publications (e.g., by USGS), due to the paucity of historical tsunami observations in this area, the associated tsunami hazard and its recurrence probability are largely unknown. To estimate the latter, in earlier work, we developed, validated with field data, and applied a Monte Carlo simulation (MCS) approach (Grilli et al., Marine Geology, vol. 264, p74, 2009) to the upper USEC (north of New Jersey). Here, a similar methodology is applied to the entire USEC. In the present MCSs, distributions of relevant parameters (e.g., seismicity, sediment properties, type and location, volume, and dimensions of slide, water depth) are used to perform large numbers (O(10 5 )) of stochastic stability analyses of submerged slopes (along actual shelf transects), based on standard pseudo-static limit equilibrium methods. The predicted SMF types (i.e., translational or rotational), surface area, and slope angle are found to match published field data quite well along the USEC. For each parameter configuration found to be unstable under a specified ground acceleration (of given return period), the tsunami source characteristic height, and corresponding runup distribution on nearby shores, are calculated using empirical equations based on earlier numerical simulation work. A final statistical analysis of generated runup values yields estimates of overall coastal hazard, from 100 and 500-yr SMF tsunami events. The latter allows identifying regions of the USEC with elevated hazard (and related SMF parameters), where complete and detailed SMF tsunami simulations should be performed. The latter will be the object of the continuation of this NTHMP work, in which inundation from SMF tsunamis thus identified will be combined with that from other tsunami sources, to develop a series of tsunami inundation maps for areas of elevated tsunami hazard along the USEC.
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A Monte Carlo approach for estimating tsunami hazard from submarine mass failure along the U.S. East coast Chris Baxter1, Stephan Grilli1, and Teresa Krause1 1 Department of Ocean Engineering, University of Rhode Island, Narragansett, Rhode Island, USA Abstract This work is being conducted as part of the development of tsunami inundation maps for the U.S. East Coast (USEC), as mandated by the National Tsunami Hazard Mitigation Program (NTHMP). Along the USEC, which borders the Atlantic Ocean Basin, tsunami hazard may result from large distant co-seismic sources (e.g., in the Puerto Rico Trench or the Azores convergence zone) or volcanic flank collapse sources (e.g., in the Canary Islands). More importantly, however, tsunami hazard may result from Submarine Mass Failures (SMFs) occurring along the nearby continental shelf break and slope (e.g., 1929 Grand Bank). Indeed, potentially large tsunamigenic SMFs can be triggered by moderate seismic activity, such as could occur along the USEC, and cause large local tsunamis. While many past SMFs have been identified along the USEC and described in various publications (e.g., by USGS), due to the paucity of historical tsunami observations in this area, the associated tsunami hazard and its recurrence probability are largely unknown. To estimate the latter, in earlier work, we developed, validated with field data, and applied a Monte Carlo simulation (MCS) approach (Grilli et al., Marine Geology, vol. 264, p74, 2009) to the upper USEC (north of New Jersey). Here, a similar methodology is applied to the entire USEC. In the present MCSs, distributions of relevant parameters (e.g., seismicity, sediment properties, type and location, volume, and dimensions of slide, water depth) are used to perform large numbers (O(105)) of stochastic stability analyses of submerged slopes (along actual shelf transects), based on standard pseudo-static limit equilibrium methods. The predicted SMF types (i.e., translational or rotational), surface area, and slope angle are found to match published field data quite well along the USEC. For each parameter configuration found to be unstable under a specified ground acceleration (of given return period), the tsunami source characteristic height, and corresponding runup distribution on nearby shores, are calculated using empirical equations based on earlier numerical simulation work. A final statistical analysis of generated runup values yields estimates of overall coastal hazard, from 100 and 500-yr SMF tsunami events. The latter allows identifying regions of the USEC with elevated hazard (and related SMF parameters), where complete and detailed SMF tsunami simulations should be performed. The latter will be the object of the continuation of this NTHMP work, in which inundation from SMF tsunamis thus identified will be combined with that from other tsunami sources, to develop a series of tsunami inundation maps for areas of elevated tsunami hazard along the USEC.
Proposed Methodology for a Probabilistic Tsunami Hazard
Assessment from Submarine Mass Failures
on the U.S. East Coast
Christopher D.P. Baxter, Stephan GrilliChristopher D.P. Baxter, Stephan GrilliDepts. of Ocean/Civil and Environmental Engineering
NRC/USGS Workshop on Landslide Tsunami ProbabilityAugust 19, 2011
Acknowledgements
• National Oceanic and Atmospheric Administration
• Stefan Maretzski
• Oliver Taylor
• Teresa Krause
Grilli, S.J., Taylor, O.-D., Baxter, C.D.P., and Maretzki, S. (2009).
Probabilistic Approach for Determining Submarine Landslide Tsunami
Hazard along the Upper East Coast of the United States, Marine Geology,
264, 74-97.
Krause, T. (2011). Probabilistic Tsunami Hazard Assessment for the East
Coast United States, M.S. Thesis, University of Rhode Island.
Outline
• Introduction
• Description of Monte Carlo Model
• Validation
• Statistical Analysis
• Model Results
• Limitations and Ways Forward
Objectives
• Develop and validate a probabilistic model to assess the
tsunami hazard potential for the U.S. East Coast
• Account for uncertainty at every step of the process
• Use this model as a screening tool for selecting more
detailed deterministic analyses
• Provide guidance for potential source volumes and
locations for deterministic analyses
Methodology – Monte Carlo Approach
• Evaluation of Slope Stability
• Geometry of the slopes
• Seismicity and overpressures as triggering
mechanisms
• Sediment properties• Sediment properties
• Estimate of Initial Characteristic Tsunami Amplitude and
Runup for Each Submarine Mass Failure at each
Coastal Point
• Statistical Analysis to Estimate 100-year and 500-year
Runup for each Coastal Point
Northern
Transects
Coastline simplified by
2400 coastal points
Southern
Transects
Seismicity Data from USGS Hazard Maps
Surficial Sediment Properties from CONMAP
Database
Limit Equilibrium Slope Stability
• Rotational failures were modeled using Modified Bishop’s Method
∑
∑
=
=
−+
∆
=I
i
iiiii
I
i
iui
r
hkWW
lS
FS
1
1
2cossin’ αα
• Translational failures were modeled using Infinite Slope Method
• Pseudostatic coefficient k is assumed to be equal to peak horizontal acceleration (PHA)
• Pore pressure ratio (Ru) based loosely on ODP 174
FS =
(γ −1)(1− Ru ) − k γ tanβ
(γ −1) tanβ + k γtanφ'
∑=
i r1 2
Tsunami Generation, Propagation, and Runup
• Generation
• Empirical Equations (Grilli and Watts, 2005 and others)
• Distribution type (normal, log normal) of the Monte Carlo random parameter selections were compared to known distribution types(Density, Depth, Length, etc.)
• Slope Stability
• Compared with results from a commercial slope stability program • Compared with results from a commercial slope stability program (SLOPE-W TM)
• Based on definitions by FEMA Guidelines for Coastal
Flooding Analyses and Mapping
• An event is defined to have a return period (or • An event is defined to have a return period (or
recurrence interval) of Y years, if their magnitude is
such that it is equaled or exceeded once on average
every Y years.
• The reciprocal of the return period is the probability that
the event is equaled or exceeded in any given year.
Statistical Analysis
• Pf is the probability of a tsunamigenic slope failure
• n is the total number of tsunamigenic slope failure
• N is the total number of MC simulations
n=
• The joint annual probability of a tsunamigenic slope
failure is
N
nPf =
fPHASMF PPP ⋅=
Statistical Analysis (example)
• Using 45 transects and 9,000 simulations per transect:
• N = 405,000 simulations.
• n = 62,782 tsunamigenic slope failures are recorded for ground motions corresponding to a Y = 500-yr earthquake (PPHA =1/Y = 0.2% annual-probability).
• The probability of a tsunamigenic slope failure is:
62782n
• Therefore, the joint annual probability of a tsunamigenic slope failure is:
• The reciprocal of PSMF is the return period of a tsunamigenic slope failure within the study region, i.e. 3,350 years.
• Thus, the generated runup data for each coastal point contains up to a 3,350-yr tsunami runup event.
155.015501.0405000
62782≈===
N
nPf
PSMF = PPHA ⋅ Pf = 0.002 ⋅ 0.155 ≈ 0.0003
Statistical Analysis• The design runup is defined as the 1% value of all the descending
values of runup to the highest possible return period in the study area
• To calculate the magnitude of the desired design runup for a given
coastal point:
• The values of runup collected from the tsunamigenic submarine mass failures affecting the coastal point are first sorted in mass failures affecting the coastal point are first sorted in descending order from 1 to m
• The value of runup for a given probability of exceedance (Pz) then corresponds to the zth data point, determined by: