Quake-Catcher Network
Quake-Catcher Network (QCN) was a BOINC-based volunteer computing project that turned ordinary internet-connected computers into a low-cost, distributed strong-motion seismic network. Rather than performing conventional number-crunching, QCN read the built-in or externally attached accelerometer on a volunteer's device and streamed motion data back to project servers, where it was analyzed for signs of earthquake shaking. The project ran from 5 December 2007 until its discontinuation on 1 June 2023.[1]
QCN was notable for using Micro-Electro-Mechanical System (MEMS) accelerometers already built into many laptops, as well as inexpensive external USB sensors, to approximate the coverage of a conventional strong-motion seismic network at a fraction of the cost.[2]
History
QCN was founded at Stanford University in 2007–2008 by software developer Carl Christensen, working with seismologist Elizabeth S. Cochran and Jesse F. Lawrence.[1][2] The project's BOINC application first appeared under the working names "QCN Alpha" and "QCN Alpha Test" before becoming a live production project.[3]
In 2011, Cochran received a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, awarded in large part for her role in founding QCN.[1]
Project administration subsequently moved from Stanford to the California Institute of Technology (Caltech), where it was run largely on autopilot.[1][4] In 2016, QCN was transferred to the Southern California Earthquake Center (SCEC) at the University of Southern California, with continued support from the Incorporated Research Institutions for Seismology (IRIS).[1]
QCN was formally discontinued on 1 June 2023; the project's website subsequently displayed a closure notice.[5]
Sensors and hardware
QCN accepted motion data from two broad categories of sensor:
- Internal sensors — the shock-protection accelerometers already built into many laptops, most notably Apple MacBook and IBM/Lenovo ThinkPad models, as well as the motion sensors in smartphones and tablets.[3][1]
- External USB sensors — low-cost, purpose-built accelerometers for desktop computers that lacked an internal sensor. QCN officially supported three such devices: the codemercs.com JoyWarrior 24F8, the ONavi sensor, and the MotionNode Accel.[1]
Sensors measured acceleration on three axes across a range of roughly ±2g (where g is Earth's standard gravity, 9.81 m/s2), sampled at 50 Hz.[6] Because desktop USB sensors could be fixed and oriented to true north, scientists generally preferred them over laptop-internal sensors, which could move or be reoriented by their owners.[6]
Detection algorithm
Each QCN client continuously evaluated incoming accelerometer samples for anomalous motion using a short-term-average/long-term-average (STA/LTA) triggering scheme, a standard approach in seismic event detection.[7] A trigger was declared when a computed statistic exceeded a fixed threshold, commonly expressed as a Z-score of the instantaneous acceleration relative to the recent noise floor:
where is the current acceleration sample, is the local mean (long-term average) acceleration, and is its standard deviation. When exceeded a threshold of approximately 3, the client streamed a compact packet of trigger information — including maximum amplitude and timestamp — to a QCN server for correlation against triggers from other sensors in space and time.[8] QCN sensors were best suited to detecting moderate-to-large earthquakes (roughly magnitude 5.0 and above) in populated areas, since their sensitivity was lower than that of professional-grade seismometers.[2]
Software and client
The QCN BOINC application was a "non-CPU-intensive" project by BOINC's own classification: unlike most BOINC projects, host computers did not perform significant scientific calculation, but instead acted as always-on data-collection and reporting nodes.[3] A companion Qt-based application called QCNLive let volunteers view live sensor readings, review recorded ground motion, and browse global earthquake activity.[9] The QCN client and server source code was released under the GNU Lesser General Public License (LGPL) version 3 or later, with a copyright held by IRIS covering the 2007–2016 period.[4]
QCN also shipped a BOINC screensaver, shown below.
Scientific impact
QCN sensors recorded data from a number of major earthquakes, including the 2010 magnitude 8.8 Maule, Chile earthquake, the 2010 magnitude 7.1 Darfield, New Zealand earthquake, and the 2015 magnitude 7.8 Gorkha, Nepal earthquake.[10] A Mexican deployment of the network, the Red Atrapa Sismos, was separately evaluated for its performance during large and damaging earthquakes in that country.[7]
Beyond research, QCN placed low-cost sensors in K-12 classrooms and museums as an educational and outreach tool, letting students and the public participate directly in seismic data collection.[11]
Scientific publications
The following list distinguishes papers that used data directly computed or collected through the BOINC-based Quake-Catcher Network platform, per the official BOINC publications page, from other publications about or related to the project.
Publications based on BOINC-computed data
- Cochran, E.S., Lawrence, J.F., Christensen, C. and Jakka, R.S. The Quake-Catcher Network: Citizen Science Expanding Seismic Horizons. Seismological Research Letters 80(1), 26–30 (2009).
- Yildirim, B., Cochran, E.S., Chung, A.I., Christensen, C.M. and Lawrence, J.F. On the Reliability of Quake-Catcher Network Earthquake Detections. Seismological Research Letters 86(3), 856–869 (2015).
- Dominguez, L.A., Yildirim, B., Husker, A.L., Cochran, E.S., Christensen, C.M., Cruz-Atienza, V.M. and Lawrence, J.F. The Red Atrapa Sismos (Quake Catcher Network in Mexico): Assessing Performance during Large and Damaging Earthquakes. Seismological Research Letters 86(3), 848–855 (2015).
- Cochran, E.S., Lawrence, J.F., Christensen, C. and Chung, A. A Novel Strong-Motion Seismic Network for Community Participation in Earthquake Monitoring. IEEE Instrumentation & Measurement Magazine 12(6), 8–15 (2009).
- Benson, K. et al. Study of the Network Impact on Earthquake Early Warning in the Quake-Catcher Network Project. Procedia Computer Science (ICCS 2014), presented at the International Conference on Computational Science 2014.
- Neighbors, C., Liao, E.J., Cochran, E.S., Funning, G.J., Chung, A.I., Lawrence, J.F., Christensen, C., Miller, M., Belmonte, A. and Sepulveda, H.H.A. Investigation of the High-Frequency Attenuation Parameter, Kappa, from Aftershocks of the 2010 Mw8.8 Maule, Chile Earthquake. Geophysical Journal International 200, 200–215 (2015).
See also
References
- ↑ 1.0 1.1 1.2 1.3 1.4 1.5 1.6 Quake-Catcher Network. Wikipedia. Retrieved 2 July 2026.
- ↑ 2.0 2.1 2.2 (2009).The Quake-Catcher Network: Citizen Science Expanding Seismic Horizons. Seismological Research Letters. pp. 26–30. DOI: 10.1785/gssrl.80.1.26.
- ↑ 3.0 3.1 3.2 Quake Catcher Network – Seismic Monitoring. BOINC. Retrieved 2 July 2026.
- ↑ 4.0 4.1 VCTLabs/qcn: Quake-Catcher Network. GitHub. Retrieved 2 July 2026.
- ↑ (26 June 2023).Quakecatcher. Wayback Machine archive of quakecatcher.net. Retrieved 2 July 2026.
- ↑ 6.0 6.1 Quakecatcher — Sensors. quakecatcher.net. Retrieved 2 July 2026.
- ↑ 7.0 7.1 (2015).The Red Atrapa Sismos (Quake Catcher Network in Mexico): Assessing Performance during Large and Damaging Earthquakes. Seismological Research Letters. pp. 848–855. DOI: 10.1785/0220140195.
- ↑ Earthquake Detection at the Edge: IoT Crowdsensing Network. arXiv. Retrieved 2 July 2026.
- ↑ Quake Catcher Network — Topics by Science.gov. Retrieved 2 July 2026.
- ↑ Crowd-Sourcing Seismic Data for Education and Research Opportunities with the Quake-Catcher Network. NASA/ADS. Retrieved 2 July 2026.
- ↑ Quake-Catcher Network. SciStarter. Retrieved 2 July 2026.