On the powerful use of simulations in the Quake-Catcher Network to efficiently position low-cost earthquake sensors

• We present the Quake-Catcher Network (QCN), a network of low-cost sensors connected to computers to monitor seismic events.
• We present the EmBOINC software to simulate QCN features and study the optimal location of sensors.
• We empirically compare QCN data and the EmBOINC simulated data and show their similarities.
• We monitor 20 earthquake scenarios with different sensor densities and earthquake magnitudes.
• We show when, by capturing P-waves, sensors can alarm people before destructive S-waves arrive.

The Quake-Catcher Network (QCN) represents a paradigm shift in seismic networks by involving the general public in the collection, detection, and recognition of seismic events. The QCN uses low-cost sensors connected to volunteer computers across the world to monitor seismic events. The location and density of these sensors can impact the accuracy of event detection. Testing different arrangements of new sensors could disrupt the currently active project; thus such an experiment is best accomplished in a simulated environment.This paper presents an accurate and efficient framework for simulating low-cost QCN sensors and identifying their most effective locations and densities. To build the framework, we extend EmBOINC, an emulator of Berkeley Open Infrastructure for Network Computing (BOINC) projects, to handle the trickle messages generated by sensors connected to volunteer hosts and sent to the QCN server when strong ground motion is detected. EmBOINC allows us to rigorously study QCN simulations at 100,000 or even 1,000,000 sensors, highlight strengths and weaknesses of different sensor density and placement, and test the network with various parameters, conditions, and earthquake scenarios. Results obtained with EmBOINC and presented in this paper show how our simulations can reliably study diverse sensor densities and seismic scenarios under different geographical and infrastructural constraints.