Projects
Real Time Seismic Waveform Quality Analysis
PI: Allen Husker (Division of Geological and Planetary Sciences)
SASE: Jade Kessinger, Scholar
Real-time monitoring of seismic waves is essential for both basic science and providing services for public safety. In response, dense seismic networks have been developed. The Southern California Seismic Network (SCSN) is one of the largest and longest running regional seismic networks in the United States. The data collected by this network serves a critical role in public safety by providing earthquake information to the West Coast Earthquake Early Warning system, which sends alerts within seconds of an earthquake. As early detection of seismic signals is an important goal, SCSN has a large station density (> 600 stations) throughout southern California. Individual stations must deliver high quality seismic data in real time 24/7 and the network records over 30 GB each day in the form of 16,000+ waveform channels which are archived at its data center. Moreover, SCSN also collect real time data from a distributed acoustic sensor (DAS) array (from long fiber optic cables) such as the one near Ridgecrest, CA. While the networks have dedicated staff for station maintenance, archival, and earthquake analysis, they have limited resources to monitor all stations for all times of the day. Some issues might be revealed as trends over time, making them hard to detect with spot inspection. This has led to delay in discovery of station issues. Furthermore, once an issue is detected or resolved, determining how downstream applications should adjust can be a time-consuming process. For these reasons, the SCSN would benefit from further automation and data visualization tools to manage the large number of stations and processes it operates. Such a development project would not only maximize the amount of high-quality data processed and archived but provide a robust platform to incorporate new algorithms and technologies in its 24/7 production environment.
Seismic stations making up the Southern California Seismic Network (SCSN) along with faults (shown with black lines)
In collaboration with the Software Academy, SCSN is developing an automated waveform quality monitoring framework for seismic and Distributed Acoustic Sensing (DAS) data. This system is being built on existing analysis tools. The Scholar is unifying these tools into a single platform, collecting metrics, and archiving them, while providing a web interface where metrics can be plotted over time. The Scholar will also develop automated workflows based on the results of waveform analysis. The success of this project would enhance the performance of SCSN network and the ShakeAlert Earthquake Early Warning System, which would improve seismic hazard mitigation efforts. The improved completeness and quality of the seismic archive would benefit long term seismological research. The resulting code will be open source, and SCSN will promote the new software to other regional seismic networks to increase its impact.