The Quake-Catcher Network (QCN) is a transformative approach to earthquake detection, science, and outreach. The QCN is a distributed computing project that links internal (no cost, built-in) or external (low cost, USB-based) accelerometers connected to any participating computer for earthquake research. Leveraging an innovative set of cyber-enabled seismic observations, this approach will enable the creation of a very dense, low-cost seismic network that can explore earthquake fault rupture in real-time, establish ground response to seismic wave passage, and quantify the shaking effects on critical structures. Results from a one-year exploratory grant from the NSF Cyber-Infrastructure Teaching, Education, Advancement and Mentoring program indicate QCN has the ability to be a new and transformative type of network, which is scalable and easy to deploy world-wide. Increasing the number of QCN sensors from 1,000 to more than 30,000 worldwide and developing efficient schemes to ingest, process, and distribute Terabytes of data will allow us to (1) explore fault mechanics (including directionality, slip distribution, and rupture velocity) at unprecedented resolutions, (2) study ground motions to assess seismic hazard and building response and (3) analyze data in real time for earthquake early warning and rapid response. This proposal will result in network with 6,000 new USB sensors and tens of thousands of no-cost sensors commonly built internal to laptops and other devices. Additionally, QCN will provide the cyberinfrastructure to process and analyze the large new seismic data sets in near-real time and to foster collaboration between 1000?s of researchers and interested participants around the world. The framework laid by this project will enable rapid expansion of the network internationally and will allow us to grow the network at a fraction of the cost of traditional seismic instrumentation and infrastructure, providing valuable data to augment the existing seismic networks.The success of the Quake-Catcher Network is intrinsically linked to the broader participation of the general public; members of the public, schools (K-12, undergraduate and graduate), and community organizations host QCN sensors. These ?citizen-scientists? will receive real-time earthquake information, seismic data and results, and interactive educational materials.
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 Quake-Catcher Network (QCN) is a large network of low-cost sensors connected to computers, both desktops and laptops, owned by volunteer participants around the world to monitor seismic events. The QCN architecture is built upon the Berkeley Open Infrastructure for Network Computing (BOINC) software for volunteer computing. BOINC enables QCN hosts to efficiently report ``triggers'', i.e., indications of probable seismic activity at the host location, to a project server at Stanford University in California, USA. The location and density of the QCN sensors can impact the accuracy of event detection. Testing different arrangements of new sensors could disrupt the currently active project, thus this experiment is best accomplished in a simulated environment. With the proper sensor density and locations, QCN can augment traditional earthquake detection networks and improve their ability to quickly detect moderate to strong earthquakes, with per-sensor costs well below those of these other networks. In this project we studied the design and implementation aspects of trustworthy QCN networks of low-cost sensors. To address the problem, we designed and implemented an accurate and efficient framework for simulating the low cost QCN sensors and identifying their most effective locations and densities. To build the framework, we extended and existing simulator of volunteer computing projects to handle the trickle messages generated by sensors connected to volunteer’s hosts and sent to the QCN server when strong ground motion is detected. The simulator allowed 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. Our simulations also outlined that the master-worker topology in QCN and other similar projects is a major weakness. For example, the centralized master can fail to collect data if the volunteers' computers cannot connect to the network, or it can introduce significant delays in the warning if the network is congested. We solved the problems associated to this weakness by using multiple servers in a more advanced network topology than the simple master-worker configuration. We considered several critical scenarios modeling the 1986 Oceanside earthquake, occurs in the region of San Diego (CA). Results obtained with the simulation of these scenarios and using our framework showed how our simulations can reliably enable the study diverse sensor densities and seismic scenarios under different geographical and infrastructural constraints. We integrated the simulator in an educational tool that is hosted on Amazon AWS and is accessible to the public called QCN Explorer (http://qcnexplorer.org/). QCN Explorer allows users to study any earthquake scenario on the earth through simulations of the Quake Catcher Network in an easy-to-use and friendly browser environment. The goal of this interface is to educate people about seismology and to increase the awareness on the science beyond earthquake monitoring and predictions. For example, QCN Explorer allows users to simulate how QCN responds to an earthquake with a larger number of sensors than the network currently supports. The simulator is free for anyone to use and will be maintained online beyond the duration of the project.
