Automatic event detection and location using feature weighted beamforming
Andrew Reynen, Kit Chambers, Dario Baturan · 2018
Seismic catalogs yielding the location and origin time of events serve as the foundation to risk management and public safety strategies, where the accuracy and response time of the catalog creation system is important. In a scenario where low magnitude events contribute heavily into real-time decision making, conventional autopicking and association techniques can yield large origin time and location differences due to the inclusion of noise picks. Here we present a new technique, Feature Weighted Beamforming (FWB), which can be applied in near real-time to increase the accuracy of automatically generated catalogs, while maintaining the systems sensitivity. This method has been tested on multiple surface arrays ranging in radius from 5 to 250 km, and number of stations between 6 and 90. All tests have shown similar results for location accuracy improvements and noise event removal. The results from the private Duvernay Subscriber Array are given here. As compared to standard STA/LTA picking with subsequent associations, FWB reduced the number of false positives by 75% as well as reducing the average difference in the event location between automatic and manually picked solutions by 63%. Presentation Date: Tuesday, October 16, 2018 Start Time: 1:50:00 PM Location: Poster Station 2 Presentation Type: Poster