Quake-Catcher Network

Quake-Catcher Network
A MEMS accelerometer chip of the type used by Quake-Catcher Network host sensors
Project
Status	Completed
Category	Earth and Environmental Science, Seismology
Compute	CPU
Requires	MEMS accelerometer (internal laptop sensor or external USB sensor)
Development
Developer	Carl Christensen (Stanford University); later Southern California Earthquake Center (SCEC), University of Southern California
Author	Carl Christensen
Sponsor	National Science Foundation, Incorporated Research Institutions for Seismology (IRIS), Southern California Earthquake Center
Maintainer	Southern California Earthquake Center / University of Southern California (project); Stephen L. Arnold (client code, later years)
Initial release	December 5, 2007  (19 years ago)
Completed	June 1, 2023  (3 years ago)
Repository	https://github.com/VCTLabs/qcn
Software
Written in	C++, Qt (QCNLive client)
Operating system	Windows, Linux, macOS, Android
BOINC statistics
Stats as of	November 1, 2022  (4 years ago)
Performance	0.02 GigaFLOPS
Total users	11,321
Total hosts	16,167
Metadata
Website	https://quakecatcher.net/
License	LGPL-3.0-or-later

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.

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.

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. The project's BOINC application first appeared under the working names "QCN Alpha" and "QCN Alpha Test" before becoming a live production project.

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.

Project administration subsequently moved from Stanford to the California Institute of Technology (Caltech), where it was run largely on autopilot. 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).

QCN was formally discontinued on 1 June 2023; the project's website subsequently displayed a closure notice.

Sensors and hardware

An external USB-connected MEMS accelerometer of the type used with desktop computers that lacked a built-in sensor

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.
• 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.

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. 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.

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. 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:

$Z=\frac{x-\mu }{\sigma }$

where $x$ is the current acceleration sample, $\mu$ is the local mean (long-term average) acceleration, and $\sigma$ is its standard deviation. When $Z$ 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. 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.

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. A companion Qt-based application called QCNLive let volunteers view live sensor readings, review recorded ground motion, and browse global earthquake activity. 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.

QCN also shipped a BOINC screensaver, shown below.

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Quake-Catcher Network screensaver

Scientific impact

USGS shake-intensity map of the 27 February 2010 magnitude 8.8 Maule, Chile earthquake, one of several major events recorded by QCN sensors

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. A Mexican deployment of the network, the Red Atrapa Sismos, was separately evaluated for its performance during large and damaging earthquakes in that country.

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.

Scientific publications

The following list distinguishes papers that used data directly computed or collected through the BOINC-based Quake-Catcher Network platform, as listed on the official BOINC publications page, from other publications about or related to the project.

Publications based on BOINC-computed data

• (2020).Citizen Seismology in Taiwan: Development, Outreach, and Formative Assessment of Near-Real Time Earthquake Game Competition Activities. Frontiers in Earth Science. DOI: 10.3389/feart.2020.00154.
• (2019).Performance Evaluation of Low-Cost Seismic Sensors for Dense Earthquake Early Warning: 2018–2019 Field Testing in Southwest China. Sensors. pp. 1999. DOI: 10.3390/s19091999.
• (2017).Citizen Earthquake Science in Taiwan: From Science to Hazard Mitigation. Journal of Disaster Research. DOI: 10.20965/jdr.2017.p1174. (with Academia Sinica Institute of Earth Sciences, National Taiwan Normal University Department of Earth Sciences, and Central Weather Bureau Seismological Center)
• (2015).The Red Atrapa Sismos (Quake-Catcher Network in Mexico): Assessing Performance during Large and Damaging Earthquakes. Seismological Research Letters. pp. 848–855. DOI: 10.1785/0220140171.
• (2015).Strong-Motion Observations of the M 7.8 Gorkha, Nepal, Earthquake Sequence and Development of the N-SHAKE Strong-Motion Network. Seismological Research Letters. pp. 1533–1539. DOI: 10.1785/0220150146. et al.
• (2015).Improved Rapid Magnitude Estimation for a Community-Based, Low-Cost MEMS Accelerometer Network. Bulletin of the Seismological Society of America. pp. 1314–1323. DOI: 10.1785/0120140232.
• (2015).Investigation of the High-Frequency Attenuation Parameter, κ (kappa), from Aftershocks of the 2010 Mw 8.8 Maule, Chile Earthquake. Geophysical Journal International. pp. 200–215. DOI: 10.1093/gji/ggu390. et al.
• (2015).On the Reliability of Quake-Catcher Network Earthquake Detections. Seismological Research Letters. pp. 856–869. DOI: 10.1785/0220140218.
• (2014).Rapid Earthquake Characterization Using MEMS Accelerometers and Volunteer Hosts Following the M 7.2 Darfield, New Zealand, Earthquake. Bulletin of the Seismological Society of America. pp. 184–192. DOI: 10.1785/0120120196. et al.
• (2013).On the Powerful Use of Simulations in the Quake-Catcher Network to Efficiently Position Low-Cost Earthquake Sensors. Future Generation Computer Systems. DOI: 10.1016/j.future.2013.04.012.
• (2011).The Quake-Catcher Network Rapid Aftershock Mobilization Program Following the 2010 M 8.8 Maule, Chile Earthquake. Seismological Research Letters. pp. 526–532. DOI: 10.1785/gssrl.82.4.526. et al.
• (2009).The Quake-Catcher Network: Citizen Science Expanding Seismic Horizons. Seismological Research Letters. pp. 26–30. DOI: 10.1785/gssrl.80.1.26.
• (2009).A Novel Strong-Motion Seismic Network for Community Participation in Earthquake Monitoring. IEEE Instrumentation & Measurement Magazine. DOI: 10.1109/MIM.2009.5338255.

Other project-related publications

• Study of the Network Impact on Earthquake Early Warning in the Quake-Catcher Network Project. Procedia Computer Science (ICCS 2014). Retrieved 2 July 2026.

See also

• BOINC
• SETI@home
• Einstein@Home
• Volunteer computing
• Southern California Earthquake Center