Fakequakes: generating kinematic rupture scenarios and synthetic displacement data, an example application to the Cascadia subduction zone.

Diego Melgar

UC Berkeley

Date & Time
Building 3, Room 3240 (main USGS conference room)
Jessica Murray

Earthquake and tsunami early warning algorithms that rely on geodetic data for rapid assessment of large sources are becoming widespread. They are, however, not often exercised because GNSS data have noise-levels in the ~2-3cm range and thus are only sensitive to Mw6.5+ events. As a result it is difficult to assess the reliability and performance of these algorithms. I will show an application of the Karhunen-Loève expansion to generate stochastic slip distributions of large events with an example application to the Cascadia subduction zone. I'll discuss how to extend the static slip distributions obtained from the K-L expansion method to produce kinematic rupture models and generate synthetic long-period displacement data at the sampling rates of traditional GNSS stations. I'll discuss how to validate the waveforms produced by this method by way of comparison to a displacement based GMPE based on peak ground displacement measurements from GNSS measurements of large earthquakes worldwide. The goal is that this method be used to generate synthetic waveforms for hundreds of events that can be used as diagnostic tests of geodetic based earthquake and tsunami early warning algorithms.

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Video Podcast