CI-TEAM Implementation Project: The Quake-Catcher Network, a low-cost, high-density strong motion network

加州大学河滨分校和斯坦福大学正在合作推进地震监测网络（QCN）的开发，这是一个新的分布式地震监测网络，利用三轴加速度计（已成为许多笔记本电脑的标准设备）和一个“公民科学家”志愿者社区。 这些加速度计的低成本和小尺寸使得能够创建廉价、密集的网络，以补充大洛杉矶盆地中的现有地震网络。该网络还为促进K-12教室内的地球科学教育提供了一个廉价的工具，为学生提供了进行动手，基于调查的调查的机会。 通过使用伯克利开放式网络计算基础设施（BOINC）分布式软件处理包实现联网，当与传感器采集软件相结合时，可以对地震数据进行超高效的处理、传输和分析。 监测加速度计的志愿者在检测到强烈的新运动时向中央服务器发送小数据包;波形数据仅在确认地震时上传。该规划补助金将用于完成QCN计算软件的平台独立版本的开发;通过外联增加网络中的志愿者人数;完成K-12教室内使用的交互式软件的开发;向K-12学校分发200个USB加速计和教育材料;以及评估通过网络提供的数据。

The University of California-Riverside and Stanford University are collaborating to advance development of the Quake-Catcher Nework (QCN), a new distributed network for earthquake monitoring that utilizes 3-axis accelerometers, which have become standard equipment in many laptop computers, and a volunteer community of 'citizen scientists'. The low cost and small size of these accelerometers enables creation of an inexpensive, dense network that supplements existing seismic networks in the Greater Los Angeles Basin. The network also provides an inexpensive vehicle for promoting geoscience education within K-12 classrooms that provides students with the opportunity to conduct hands-on, inquiry-based investigations. Networking is achieved through the use of the Berkeley Open Infrastructure for Network Computing (BOINC) distributed software processing package, which allows, when combined with the sensor acquisition software, ultra-efficient processing, transfer, and analysis of the seismic data. Volunteers monitoring the accelerometers send small data packets to the central server when strong new motions are detected; waveform data only uploads when an earthquake is confirmed. This planning grant is being used to finish development of the platform independent version of the QCN computing software; increase the number of volunteers in the network through outreach; complete development of interactive software for use within K-12 classrooms; distribute 200 USB accelerometers and educational materials to K-12 schools; and evaluate data contributed through the network.