- © 2009 by the Seismological Society of America
The Quake-Catcher Network (QCN) is a seismic network that implements distributed/volunteer computing with the potential to provide critical earthquake information by filling in the gaps between traditional seismic stations. Microelectromechanical systems (MEMS) sensors detect vibrations within the frequency range of local seismic waves (0.1–20 Hz), so any internet-connected computer with an internal or external MEMS accelerometer can become a strong-motion seismic station. The QCN, a distributed computing project, uses idle computer cycles and MEMS sensors to increase the number of seismic stations, which may soon provide faster and more accurate detection and characterization of moderate to large earthquakes. We present accelerograms and triggering analysis of an Mb 5.1 earthquake recorded by laptop MEMS accelerometers during early testing of the QCN system. In addition, we present here the advantages of distributed computing and MEMS accelerometers for seismic monitoring, as well as basic triggering algorithms.
The QCN capitalizes on the main advantage of distributed computing—achieving large numbers of processors with low infrastructure costs—to provide a dense, large-scale seismic network. While MEMS accelerometers are less sensitive than typical broadband or short-period sensors, a higher number of stations is advantageous for both the study of earthquakes and, potentially, earthquake early warning (Allen and Kanamori 2003; Wurman et al. 2007). Volunteer computing reduces overhead by limiting instrument, operation, and maintenance costs associated with traditional seismic networks (Anderson et al. 2002).
Distributed computing brings many advantages to the field of seismology. Data are analyzed on an individual's laptop or desktop, and only minimal data are transferred to a central server for further analyses. This differs from the traditional approach of uploading continuous waveform data to a central server for analysis (Allen and Kanamori 2003; Wurman et al. 2007). By pushing the analysis to the sensor level, a greater volume of seismic data …