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A Focal Mechanism Catalog for Southern California Derived with Deep Learning Algorithms

We present an updated focal mechanism catalog for 129,430 events that occurred in southern California from 2011 to 2018. The approach uses S/P amplitude ratios and first-motion P wave polarities to determine focal mechanisms with the HASH method of Hardebeck and Shearer (2002, 2003). In addition to the broadband channels used by Yang et al. (2012), three-component short period channels (EH-) are also used to obtain P- and S-wave amplitudes. Moreover, we utilize a convolutional-neural-network (CNN) phase picker (Ross et al., 2018b) and polarity picker (Ross et al., 2018a), which are trained on millions of manually labelled seismograms from Southern California, to detect more phase arrivals and polarities and further improve the focal mechanism catalog. For all events, we detect additional P- and S-wave arrivals using the CNN phase picker to obtain more S/P amplitude ratios. For 126,999 small magnitude events (M < 2.8), extra CNN P-wave first-motion polarities are also added into focal mechanism calculations. More than 60% of the events have at least 5 additional polarities and S/P amplitude ratios compared with Yang et al., leading to 63% more (59,679) mechanisms than 36,668 focal mechanisms in Yang et al. (2012), with >90% quality A and B common events having rotation angles less than 35 degree, which is the uncertainty value used to define quality B focal mechanism. The number of quality B, C and D focal mechanisms increased by 43%, 84% and 63%, respectively. We also apply CNN pickers to 65 co-located events with similar waveforms as well as 782 M > 3 events with moment tensor solutions to test the accuracy improvement. The solved focal mechanisms are more consistent after implementing CNN pickers. The results indicate that CNN pickers can significantly improve the quality and quantity of the obtained focal mechanisms. The procedure will be applied to the rest of the catalog events in southern California to improve the focal mechanisms data base and related analysis results.

Presenting Author: Yifang Cheng

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