Annual Meeting - SCEC.org

Group 1 – SHRA | Poster Abstracts
1-033
DISTRIBUTED COMPUTING AND MEMS ACCELEROMETERS: THE QUAKE
CATCHER NETWORK Cochran ES, Lawrence JF, Christensen C, and Jakka RS
Recent advances in distributed computing provide exciting opportunities for seismic data
collection. We are in the early stages of implementing a high density, low cost strong-motion
network for rapid response and early warning by placing accelerometers in schools, homes, and
offices. The Quake Catcher Network (QCN) employs existing networked laptops and desktops to
form a dense, distributed computing seismic network. Costs for this network are minimal because
the QCN uses 1) strong motion sensors (accelerometers) already internal to many laptops and 2)
low-cost universal serial bus (USB) accelerometers for use with desktops. The Berkeley Open
Infrastructure for Network Computing (BOINC!) provides a free, proven paradigm for involving
the public in large-scale computational research projects. In the first six months of limited release of
the QCN software, we successfully received triggers and waveforms from laptops near the M 4.7
April 25, 2008 earthquake in Reno, Nevada and the M 5.4 July 29, 2008 earthquake in Chino,
California.
Engaging the public to participate in seismic data collection is not only an integral part of the
project, but an added value to the QCN. The software will provide the client-user with a screensaver
displaying seismic data recorded on their laptop, recently detected earthquakes, and general
information about earthquakes and the geosciences. Furthermore, this project will install USB
sensors in K-12 classrooms as an educational tool for teaching science. Through a variety of
interactive experiments students will learn about earthquakes and the hazards earthquakes pose.
1-034
COMPLETENESSWEB: AN ONLINE RESOURCE FOR SEISMIC NETWORK
COMPLETENESS DATA Schorlemmer D, and Euchner F
Reliable estimates of the spatio-temporal evolution of detection completeness of seismic networks
and the catalogs compiled by these networks are an essential prerequisite for almost any study in
earthquake statistics. We present a new online resource for completeness data computed using the
probability-based magnitude of completeness (PMC) method. Raw completeness data, datasets in
plotting-friendly format, and maps of completeness magnitude and detection probabilities are
available through a RESTful web service that is easy to consume from the command line. The raw
data sets contain detection probabilities for a set of magnitude levels, and completeness
magnitudes for different probability levels. They are encoded in an XML-based format. Spatial
maps of these values are provided in publication-ready quality. The web service is accompanied by
a web site providing a detailed method description, and codes and data sets which allow for
reproducing the presented results, see completeness.usc.edu. Currently, completeness data of the
Southern California Seismic Network for the years 2001-2007 are provided. Data of the Northern
California Seismic Network, the Italian National Seismic Network, the Friuli Network (Italy), the
Swiss Digital Seismic Network, and the Network of the Japanese Meteorological Agency are in
preparation by different groups of researchers.
1-035
CREATING GLOBAL-SCALE SEISMIC HAZARD MAPS WITH OPENSHA AND
PARALLEL COMPUTING Milner KR, Meyers DP, Field E, Callaghan S, Ogale M, Juve G,
Maechling PJ, and Jordan TH
Using parallel computing resources at high performance computing (HPC) centers across the
country, including USC HPCC, the National Center for Supercomputer Applications (NCSA), and
2008 SCEC Annual Meeting | 85