Annual Meeting - SCEC.org

S. Hiemer, Q. Wang, D.D. Jackson, Y.Y. Kagan, S. Wiemer, J.D. Zechar, and J. Woessner
Poster Abstracts
We are developing a stochastic earthquake source model appropriate for intermediate to long-term forecasts, expressed in
terms of earthquake rate per unit area-time-magnitude-focal mechanism direction. We start with what is known best: the
frequency-magnitude distribution, slip rates on major faults, long-term strain rate, and instrumentally recorded and historic
earthquake catalogs. We illustrate our approach with an application to California. We use the stochastic earthquake source
model to simulate hazard-relevant earthquakes. The resulting simulated earthquakes are represented by spatially tapered
ruptures on hypothetical rectangular fault planes with specified length, width, strike, dip, and rake. Their locations and
orientations approximate those of mapped faults based on empirical studies as well as historical and instrumental seismicity
records. Many mapped faults are located only after major earthquakes occur on them, and many 20th century earthquakes
occurred on previously unrecognized faults. We identify testable features of the model and devise quantitative prospective
and/or retrospective tests as appropriate for parameter optimization.
STATION-TO-STATION GREEN’S FUNCTIONS EXTRACTED FROM SEISMIC CODA IN SOUTHERN
CALIFORNIA (B-054)
E.T. Hirakawa and S. Ma
It has been demonstrated recently that correlation of seismic diffuse field (ambient noise and coda) recorded at two stations
leads to deterministic station-to-station Green’s functions. The theory requires a homogeneous distribution of uncorrelated
sources or scatters. However, this condition is not satisfied by the ambient noise data, which is dominated by the microseisms.
In this study, we will explore the seismic coda recorded in southern California for over 10 years. In contrary to ambient
seismic noise, coda waves, generated by multiple scattering by small-scale heterogeneities, are independent of the sources that
generate them. The Green’s function extracted from coda waves should approach more closely to the true Green’s functions.
These Green’s functions will provide a way to quantify the bias in these two different data sets. We will also explore the
frequency content and amplitude of coda-wave Green’s functions and compare them with numerical Green’s functions based
on the current Community Velocity Models of southern California.
CREATING PROBABILISTIC SEISMIC HAZARD MAPS FROM CSEP DATA (B-125)
J.R. Holliday
One of the loftier goals in seismic hazard analysis is the creation of an end-to-end earthquake prediction system: a "rupture to
rafters" work flow that takes a prediction of fault rupture, propagates it with a ground shaking model, and outputs a damage
or loss profile at a given location. So far, the initial prediction of an earthquake rupture (either as a point source or a fault
system) has proven to be the most difficult and least solved step in this chain. However, this may soon change.
The Collaboratory for the Study of Earthquake Predictability (CSEP) has amassed a suite of earthquake source models for
assorted testing regions worldwide. These models are capable of providing rate-based forecasts for earthquake (point) sources
over a range of time horizons. Furthermore, these rate forecasts can be easily refined into probabilistic source forecasts. While
it's still difficult to fully assess the "goodness" of each of these models, progress is being made: new evaluation procedures are
being devised and earthquake statistics continue to accumulate. The scientific community appears to be heading towards a
better understanding of rupture predictability. It is perhaps time to start addressing the second step in the earthquake
prediction system.
In this poster, I show how a simple ground shaking model can be constructed and applied directly to CSEP formatted source
output. As a case study, I present probabilistic seismic hazard maps for peak ground acceleration (PGA) in the state of
California using sixteen models supplied to the Regional Earthquake Likelihood Models (RELM) experiment. Furthermore, I
give a full PGA exceedance profile for Palm Springs as calculated by each of the sixteen input models.
2011 SCEC Annual Meeting | 177