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New Scaling Relationships of

Earthquake Source Parameters for

Stochastic Tsunami Simulations

Katsu Goda

Senior Lecturer in Civil Engineering

University of Bristol

United Kingdom

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Probabilistic Tsunami Risk Analysis

Scenarios &

source region

characteristics

Scenario

generation

Exposure

Scenarios may

be specified

based on

inversion

models

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Hazard

modelling

Vulnerability

Consequences

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Scaling Relationships for Source Parameters

• Earthquake source modelling aims at predicting key characteristics

of a fault rupture based on past major earthquakes.

• The results are typically summarised as empirical scaling

relationships (Wells and Coppersmith 1994; Murotani et al. 2013).

• The source parameters of interest include fault geometry, slip

statistics, and spatial slip distribution.

• Currently, there is a critical gap in stochastic earthquake source

modelling: lack of comprehensive evaluations of the spatial slip

distribution parameters (also applicable to geometry/slip statistics

parameters) for large mega-thrust subduction earthquakes.

• New scaling relationships are useful for characterising uncertainties

of earthquake source characteristics in strong motion and tsunami

simulations.

bristol.ac.uk/cabot Living with environmental uncertainty 5

SRCMOD Database

• SRCMOD

(http://equake-

rc.info/srcmod/) is a

comprehensive and

growing on-line

database of finite-

fault rupture models

• It includes 317

inversion-based

rupture models

from 155

earthquakes as of

December 2015.

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Analysis of Finite-fault Rupture Model

• Numerous

source models

are analysed

consistently and

uniformly.

• Effective

dimension

analysis -> Box-

Cox analysis ->

Spectral analysis

12 )1()(

H

zx

k

AAkP

von Karman model:

Estimated

parameters:

width, length, area,

mean slip, max slip,

Box-Cox power,

correlation lengths,

and Hurst number

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Fault Geometry Parameters

• The prediction model of the fault width for subduction events

differs from existing scaling relationships.

• The different behaviour is related to the dip angle of the fault plane

and lower limit of the seismogenic thickness.

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Slip Statistics Parameters

• Both mean and maximum slips for subduction models differ

significantly from those for non-subduction models.

• These two parameters are highly correlated.

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Spatial Slip Distribution Parameters

• The prediction model of the correlation length for subduction

events differs from an existing scaling relationship.

• This is similar to the fault width.

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Spectral Synthesis of Slip Distribution

(i) Define a scenario and fault model.

(ii) Generate source parameters from scaling

models.

(iii) Generate a slip distribution and determine

its position within the fault plane.

(iv) Perform tsunami simulation

(v) Repeat (i) to (iv) for probabilistic hazard

analysis.

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Synthesised Stochastic Source Models

• A Mw9.0 scenario is

considered in the

Tohoku region of

Japan.

• Two cases are set up

for the inclusion or

exclusion of prediction

errors of the scaling

models.

• Various source models

are generated for

probabilistic hazard

analysis.

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Tsunami Hazard Assessment

• Uncertainty associated with tsunami

hazard analysis can be quantified

and visualised through various

graphical plots.

• These are useful for making tsunami

risk management decisions.

Probability distribution of

inundation area above 3 m

depth for the two cases

Spatial variation of tsunami

height along the coast for

the two cases

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Tsunami Hazard Maps

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Summary

• New scaling relationships of the earthquake source parameters

enable probabilistic earthquake-tsunami hazard-risk assessments

by accounting for detailed spatial slip characteristics.

• The new models are particularly applicable to mega-thrust

subduction earthquakes.

• The new models are multivariate prediction models of the source

parameters (variability and dependency are modelled).

• The stochastic tsunami simulation can be carried out using the new

models.

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Future Research Topics

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Ongoing Research Development

• Applications of stochastic earthquake-tsunami hazard and risk

methods to different subduction regions: Nankai-Tonankai (Japan),

Sumatra (Indonesia), Guerrero (Mexico), Cascadia (Canada), etc.

• Multi-variate modelling of building vulnerability: peak ductility and

residual ductility for shaking vulnerability and inundation depth and

flow velocity for tsunami vulnerability.

• Analytical earthquake-tsunami fragility modelling of structures.

• Probabilistic coupled earthquake-tsunami hazard-risk analysis.

• Multi-hazard insurance and catastrophe bonds – what is the tipping

point of shaking related loss and tsunami related loss? This also

requires different modes of emergency responses.

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Nankai-Tonankai Earthquake

• The Nankai-Tonankai earthquake is anticipated to occur in the near

future.

• The new tsunami source models developed by the Cabinet Office do

not take full account of uncertainties associated with the rupture

scenarios.

• The stochastic source

models for the

Nankai-Tonankai

event are developed

for hazard and risk

prediction purposes.

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• Stochastic

source models

for the Nankai-

Tonankai

earthquake.

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• Stochastic

inundation

maps for

Hamamatsu

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Coupled Simulation: Strong Motion

• Comparison of

observed and simulated

ground motions at

MYGH08 (Iwanuma)

and MYGH12

(Shizugawa).

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Coupled Simulation: Tsunami

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Any Questions?