Annual Meeting - SCEC.org

Poster Abstracts
from different angles to better identify possible offset features. Overall, the LiDAR measurements compare well within a
reasonable level of expectation with the field observations. The objective of attempting to attain a greater number of
observations was achieved, but the measurements had more uncertainty than did the field observations.
STATISTICAL ANALYSIS OF 71 BURSTS IN SOUTHERN CALIFORNIA (B-071)
X. Chen, P.M. Shearer, and R. Abercrombie
We analyze 69 out of the 71 bursts in Southern California from Vidale and Shearer [2006] with LSH catalog locations. We
apply a weighted least- squares method to model the spatial migration behavior, finding the best- fitting migration directions
and velocities. We then compute a statistical migration significance sm with a bootstrap resampling method for each burst. We
define 37 bursts with sm≥ 0.8 as the migration group, and 32 bursts with sm< 0.8 as the non-migration group. To better
distinguish the two groups, we compute a set of parameters including effective stress drop Δσ (ratio between total moment
and radius), skew of moment release time series µ, timing of largest event tmax, and distance separation between the first half
and second half of the sequence (ds). As expected, the migration group features higher ds and lower Δσ, consistent with higher
migration significance. It also features lower µ and higher tmax, similar to swarms in the Salton Trough [Chen and Shearer,
2011], while the non-migration group is more similar to mainshock-aftershock sequences. To explore possible fluid
involvement, we model the migration behavior with the fluid diffusion equation, and identify 18 bursts with diffusion
coefficients ranging from 0.01 to 0.8 m 2 /s, with the majority below 0.16 m 2 /s. The 18 bursts are mostly associated with normal
faulting mechanisms, while the non-migration group has the most reverse faulting mechanisms, indicating a possible link
between sequence type and focal mechanism.
APPROACHES FOR HIGH – PERFORMANCE WAVEFORM CROSS CORRELATION (B-073)
R.J. Chen, G.C. Beroza, and W.L. Ellsworth
Repeating earthquakes and low-frequency earthquakes exhibit very similar seismograms. Comparing waveform similarity is
an essential task for detecting either events, but it is computationally expensive due to enormous amounts of continuous data
needed to be analyzed. In this study, we examine seismograms from earthquakes on the San Andreas Fault near Parkfield and
test the efficiency of several algorithms for measuring waveform similarity. We start with the standard similarity measure,
where we take a window of a sample seismogram as a template and compare it with another seismogram using the
correlation coefficient as our benchmark. To test this, we use several approaches. First, we decrease the computations required
by using only alternate data points in the analysis to see if the results are sufficiently accurate. We then try another approach
in which we parameterize the seismograms using only the peaks and troughs as the basis for a different similarity measure
that might have sufficient accuracy. Our goal in these approaches is to develop algorithms that are sufficiently efficient to
apply to continuous waveform data.
RESULTS FROM THE QUAKE-CATCHER NETWORK RAPID AFTERSHOCK MOBILIZATION PROGRAM
(QCN-RAMP) IN CHRISTCHURCH, NEW ZEALAND (B-078)
A.I. Chung, E.S. Cochran, C. Christensen, A.E. Kaiser, and J.F. Lawrence
Following the 4 September 2010 M7.1 Darfield earthquake in New Zealand, we initiated a QCN Rapid Aftershock
Mobilization Program (RAMP). 180 low-cost 14-bit USB accelerometers were quickly installed to record as many aftershocks
as possible. Earlier in 2010, the QCN team deployed about 100 accelerometers in Chile following the M8.8 earthquake there
and began experimenting with our rapid earthquake detection and characterization program. Following the New Zealand
RAMP deployment, we continued to test this program with the more densely spaced Christchurch QCN array.
When the Christchurch event occurred on 22 February 2011, QCN sensors recorded the M6.3 event and calculated its
magnitude, location and created a map of estimated shaking intensity within 7 seconds of the earthquake. Successive
iterations improved the calculations and within 24 seconds of the earthquake, magnitude and location values comparable to
those provided from GNS were calculated.
Using the wealth of data from this event as well as the following aftershocks, we have been able to improve our detection and
identification algorithms. Whilst still in the development stages, our preliminary results from the data collected from these
and other QCN stations around the world suggest that MEMS sensors installed in homes, schools, and offices provide a way
to dramatically increase the density of strong motion observations for use in seismic hazard analysis and earthquake early
warning. We are continuing to expand our network and have recently started deploying QCN sensors to the San Francisco
Bay Area and Los Angeles areas.
152 | Southern California Earthquake Center