Thread: SSA 2020 Session - Leveraging Advanced Detection, Association and Source Characterization in Network Seismology

Dear Colleagues,

Do you create earthquake catalogs? Are you developing algorithms that can improve the production of these catalogs? Do you have a vision for the future of seismic monitoring? If so, please consider submitting to the SSA 2020 session
"Leveraging Advanced Detection, Association and Source Characterization in Network Seismology".

Leveraging Advanced Detection, Association and Source Characterization in Network Seismology
In a classic seismic monitoring framework, automatic pickers detect earthquakes, individual detections are associated into events, and events are further characterized using routine methods (e.g., single-event locators, magnitude estimators). While this processing structure underlies the operations of the majority of seismic networks, researchers continue to develop novel ways to extract additional earthquake data from continuous waveforms. Template matching is routinely applied to lower detection thresholds. Machine learning algorithms detect earthquake signals and further classify key seismic characteristics (e.g., phase-type). Multiple-event relocation algorithms retrospectively enhance earthquake hypocenter estimates. While many such techniques have vastly improved our understanding of cataloged seismicity, hurdles remain when applying these techniques to real-time systems and therefore they have not been routinely adopted. In this session, we invite submissions that investigate novel earthquake detection and characterization techniques, particularly with a focus on how these could be applied in a real-time environment to regional and global seismic networks.

Conveners:
Will Yeck, Kris Pankow, Gavin Hayes, Paul Earle, Harley Benz