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Directivity Modes of Earthquake Populations with Unsupervised Learning

Zachary E. Ross, Daniel T. Trugman, Kamyar Azizzadenesheli, & Anima Anandkumar

Published July 26, 2019, SCEC Contribution #9271, 2019 SCEC Annual Meeting Poster #083

We present a novel approach for resolving modes of rupture directivity in large populations of earthquakes. A seismic spectral decomposition technique is used to first produce relative measurements of radiated energy for earthquakes in a spatially-compact cluster. The azimuthal distribution of energy for each earthquake is then assumed to result from one of several distinct modes of rupture propagation. Rather than fitting a kinematic rupture model to determine the most likely mode of rupture propagation, we instead treat the modes as latent variables and learn them with a Gaussian mixture model. The mixture model simultaneously determines the number of events that best identify with each mode. The technique is demonstrated on four datasets in California with several thousand earthquakes. We show that the datasets naturally decompose into distinct rupture propagation modes that correspond to different rupture directions, and the fault plane is unambiguously identified for all cases. We find that these small earthquakes exhibit unilateral ruptures 53-74% of the time on average. The results provide important observational constraints on the physics of earthquakes and faults.

Key Words
Rupture directivity, machine learning, unsupervised learning, mixture models

Ross, Z. E., Trugman, D. T., Azizzadenesheli, K., & Anandkumar, A. (2019, 07). Directivity Modes of Earthquake Populations with Unsupervised Learning. Poster Presentation at 2019 SCEC Annual Meeting.

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