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2023 - Cheng, Y., Ross, Z. E., Hauksson, E. Ben-Zion, Y: Refined Earthquake Focal Mechanism Catalog for Southern California Derived with Deep Learning Algorithms (1981-2021)
The CNN_SoCal catalog is a focal mechanism catalog that implements additional phases and polarities detected using CNN algorithms (Ross et al., 2018a, b) to calculate focal mechanisms with HASH method (Hardebeck and Shearer, 2002, 2003). This catalog contains 297,478 focal mechanisms from 1981 to 2021 in southern California. The CNN_SoCal catalog is overall consistent with the standard catalog (Yang et al., 2012) but with 40% more solutions and is more consistent with the moment tensor solutions derived using waveform-fitting methods.
If you use this catalog in your work, we ask that you cite:Cheng, Y., Ross, Z. E., Hauksson, E., Ben-Zion, Y. (2023), Refined earthquake focal mechanism catalog for southern California derived with deep learning algorithms. Journal of Geophysical Research: Solid Earth, 128, e2022JB025975. doi: 10.1029/2022JB025975
Please also acknowledge the DOIs for SCSN and SCEDC as described in the Acknowledgements section.
Update: Nov 10, 2022: updated focal mechanism quality based on the following definition:
Quality A:
nodal plane uncertainty <=25
mechanism probability >=0.8
polarity misfit <=0.2
STDR >=0.4
Quality B:
nodal plane uncertainty <=35
mechanism probability >=0.6
polarity misfit <=0.3
STDR >=0.3
Quality C:
nodal plane uncertainty <=45
mechanism probability >=0.4
polarity misfit <=0.4
STDR >=0.2
Quality D:
azimuthal gap <90
take-off angle gap <90
Quality E:
other
References
Cheng, Y., Ross, Z. E., Hauksson, E., Ben-Zion, Y. (2023), Refined earthquake focal mechanism catalog for southern California derived with deep learning algorithms. Journal of Geophysical Research: Solid Earth, 128, e2022JB025975. doi: 10.1029/2022JB025975
Hardebeck, J. L., & Shearer, P. M. (2002). A new method for determining first-motion focal mechanisms. Bulletin of the Seismological Society of America, 92(6), 2264-2276, doi:10.1785/0120010200, 2002.
Hardebeck, J. L., & Shearer, P. M. (2003). Using S/P amplitude ratios to constrain the focal mechanisms of small earthquakes. Bulletin of the Seismological Society of America, 93(6), 2434-2444, doi:10.1785/0120020236, 2003.
Ross, Z. E., Meier, M. A., & Hauksson, E. (2018a). P Wave Arrival Picking and First‐Motion Polarity Determination With Deep Learning. Journal of Geophysical Research: Solid Earth, 123(6), 5120-5129, doi:10.1029/2017JB015251, 2018.
Ross, Z. E., Meier, M. A., Hauksson, E., & Heaton, T. H. (2018b). Generalized seismic phase detection with deep learning. Bulletin of the Seismological Society of America, 108(5A), 2894-2901, doi:10.1785/0120180080, 2018.
Yang, W., E. Hauksson and P. M. Shearer, Computing a large refined catalog of focal mechanisms for southern California (1981 - 2010): Temporal Stability of the Style of Faulting, Bull. Seismol. Soc. Am., June 2012, v. 102, p. 1179-1194, doi:10.1785/0120110311, 2012.
Hauksson E., W. Yang and P.M. Shearer, Waveform relocated earthquake catalog for southern California (1981 - 2011), Bull. Seismol. Soc. Am., Vol. 102, No. 5, October 2012, doi:10.1785/0120120010, 2012.
Files
1981-2021 Focal Mechanism Catalog Derived with Deep Learning Algorithms
Acknowledgements
Data and materials availability: The CNN_SoCal catalog is publicly available from the Southern California Earthquake Data Center (scedc.caltech.edu). All waveform and parametric data are available from the Caltech/USGS Southern California Seismic Network doi:10.7914/SN/CI; stored at the Southern California Earthquake Data Center, doi:10.7909/C3WD3xH1. All remaining data are available in the main text or the supplementary materials.
