Internet-Based Solutions for a Secure and Efficient Seismic Network

The Southern California Seismic Network (SCSN), operated by Caltech and USGS, leverages modern Internet-based computing technologies to provide timely earthquake early warning for damage reduction, event notification, ShakeMap, and other data products. Here we present recent and ongoing innovations in telemetry, security, cloud computing, virtualization, and data analysis that have allowed us to develop a network that runs securely and efficiently.Earthquake early warning systems must process seismic data within seconds of being recorded, and SCSN maintains a robust and resilient network of more than 350 digital strong motion and broadband seismic stations to achieve this goal. We have continued to improve the path diversity and fault tolerance within our network, and have also developed new tools for latency monitoring and archiving.Cyberattacks are in the news almost daily, and with most of our seismic data streams running over the Internet, it is only a matter of time before SCSN is targeted. To ensure system integrity and availability across our network, we have implemented strong security, including encryption and Virtual Private Networks (VPNs).SCSN operates its own data center at Caltech, but we have also installed real-time servers on Amazon Web Services (AWS), to provide an additional level of redundancy, and eventually to allow full off-site operations continuity for our network. Our AWS systems receive data from Caltech-based import servers and directly from field locations, and are able to process the seismic data, calculate earthquake locations and magnitudes, and distribute earthquake alerts, directly from the cloud.We have also begun a virtualization project at our Caltech data center, allowing us to serve data from Virtual Machines (VMs), making efficient use of high-performance hardware and increasing flexibility and scalability of our data processing systems.Finally, we have developed new monitoring of station average noise levels at most stations. Noise monitoring is effective at identifying anthropogenic noise sources and malfunctioning acquisition equipment. We have built a dynamic display of results with sorting and mapping capabilities that allow us to quickly identify problematic sites and areas with elevated noise.