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Poster Abstracts
resolutions. Overall, coarsely parameterized source reduces storage drastically, though a gain in computational time requires
an equivalent coarsening of the data, and the mean slip during the adjoint iterations converges towards the mean slip of the
true source (projected in the coarser mesh) in about 9 iterations.
DEVELOPING A PHYSICS-BASED RUPTURE MODEL GENERATOR BASED ON 1-POINT AND 2-POINT
STATISTICS OF KINEMATIC SOURCE PARAMETERS (B-002)
S. Song
A finite-fault earthquake rupture model generator (SongRMG, Ver 1.0) has been developed, based on 1-point and 2-point
statistics of key kinematic source parameters such as slip, rupture velocity, slip velocity and duration. 1-point statistics define
a marginal probability density function for a certain source parameter at a given point on a fault. 2-point statistics, i.e. autoand
cross-coherence between source parameters, control the heterogeneity of each source parameter and their coupling,
respectively. Sequential Gaussian simulation with simple kriging (SK) was initially adopted to perform stochastic modeling
(Song and Somerville, 2010), but it was replaced with a new algorithm based on the Cholesky factorization because it is
conceptually simpler to implement and also more closely linked to earthquake source inversion in the Bayesian framework,
which is an important tool in constraining the 1-point and 2-point statistics of source parameters from data. Currently the 1point
variability of source parameters follows the Gaussian distribution, but it is straightforward to transform it to any other
non-Gaussian distributions if desired. This transform will break the multi-Gaussian distribution assumed in the stochastic
modeling, but the target covariance matrix (or correlation structure) will be preserved as long as the transformed distribution
has a monotonically and smoothly increasing cumulative density function (CDF). The non-stationarity of the 1-point statistics
can also be easily implemented if necessary, such as depth dependency of the 1-point variability. Following studies will focus
on constraining input parameters in the stochastic model by dynamic rupture modeling, kinematic source inversion, and
laboratory experiments, etc., and understanding their effects on near-fault ground motion characteristics. Additionally since
the rupture model generator constrains a possible range of rupture scenarios for future events and quantify their variability
within the range, it can be used as a basis to develop an extended earthquake rupture forecast model (eERF) for fullwaveform-simulation-based
hazard analysis.
236 | Southern California Earthquake Center