Zefeng Li's home page


Postdoctoral Scholar [CV]
Seismological Laboratory
California Institute of Technology
1200 E. California Blvd., MC 252-21
So. Mudd Building, Rm 257
Pasadena, CA 91125

Phone: (626) 395-3074
Fax: (626) 564-0715
E-mail: zefengli AT caltech DOT edu

Research Interests
  1. General observational seismology, with emphasis on big seismic data and new observation/processing technologies (e.g. machine learning, distributed acoustic sensing)
  2. Seismicity, earthquake detection, earthquake early warning, fault zone structures, crustal anisotropy
Education & Employment
  1. Sep 2017 - current: Postdoctoral Scholar, Seismological Laboratory at Caltech, Pasadena, CA.
  2. Aug 2012 - Aug 2017: Ph.D. in Geophysics, Georgia Institute of Technology, Atlanta, GA.
  3. Aug 2008 - Jul 2012: B.Sc. in Geophysics, University of Science and Technology of China, Hefei, China.
Resources
  1. Matlab package of an automatic phase picker for local earthquakes: PSIRpicker and Tutorial.
  2. Shear-wave splitting measurements of local earthquakes (1995-2014) in southern California: SWS Database.
  3. Python package of high-resolution microseismic detection (local similarity based) for Large-N arrays. Available upon request.
  4. A simple Python script for for DAS channel location interpolation: DASInterp and Tutorial.
Publications
    Journal Articles:
  1. Li, Z., E. Hauksson, and J. Andrews (2019), Methods for amplitude calibration and orientation discrepancy measurement: Comparing co-located sensors of different types in Southern California Seismic Network, Bull. Seismol. Soc. Am., 109(4), 1563–1570, doi: 10.1785/0120190019. [LINK]
  2. Zhu, L., Z. Peng, J. McClellan, C. Li, D. Yao, Z. Li., and L. Fang (2019), Deep learning for seismic phase detection and picking in the aftershock zone of the 2008 Mw 7.9 Wenchuan Earthquake, Phys. Earth Planet. Inter., 293, 106261, doi: 10.1016/j.pepi.2019.05.004. [LINK]
  3. Li, Z., E. Hauksson, T. Heaton, L. Rivera, and J. Andrews (2019), Monitoring data quality by comparing co-located broadband and strong-motion waveforms in Southern California Seismic Network, Seismo. Res. Lett., 90(2A), 699-707, doi: 10.1785/0220180331. [LINK]
  4. Meier, M.-A., Z. Ross, A. Ramachandran, A. Balakrishna, S. Nair, P. Kundzicz, Z. Li, E. Hauksson, J. Andrews (2019), Reliable real-time seismic signal/noise discrimination with machine learning, J. Geophys. Res. Solid Earth, 124, 788-800, doi:10.1029/2018JB016661. [LINK]
  5. Li, Z., and Z. Zhan (2018), Pushing the limit of earthquake detection with distributed acoustic sensing and template matching: A case study at the Brady geothermal field, Geophys. J. Int., 215, 1583-1593, doi: 10.1093/gji/ggy359. [LINK]
  6. Li, C., Z. Li, Z. Peng, C. Zhang, N. Nakata, and T. Sickbert (2018), Long-period long-duration events detected by the IRIS community wavefield demonstration experiment in Oklahoma: Tremor or train signals?, Seismo. Res. Lett., 89, 1641-1651, doi: 10.1785/02201080081. [LINK]
  7. Li, Z., M.-A. Meier, E. Hauksson, Z. Zhan, and J. Andrews (2018), Machine learning seismic wave discrimination: Application to earthquake early warning, Geophys. Res. Lett., 45, 4773-4779. doi: 10.1029/2018GL077870. [LINK]
  8. Li, Z., Z. Peng, D. Hollis, L. Zhu, J. McClellan (2018), High-resolution seismic event detection using local similarity for Large-N arrays, Sci. Rep., 8, 1646. doi:10.1038/s41598-018-19728-w. [LINK]
  9. Li, Z., and Z. Peng (2017), Stress- and structure-induced anisotropy in Southern California from two-decades of shear-wave splitting measurements, Geophys. Res. Lett., 44, 9607-9614. doi: 10.1002/2017GL075163. [LINK]
  10. Li, Z., and Z. Peng (2016), An automatic phase picker for local earthquakes with predetermined locations: Combining a signal-to-noise ratio detector with 1D velocity model inversion, Seismol. Res. Lett., 87(6), 1397-1405, doi: 10.1785/0220160027. [LINK]
  11. Li, Z., and Z. Peng (2016), Automatic identification of fault zone head waves and direct P waves and its application in the Parkfield section of the San Andreas Fault, California, Geophys. J. Int., 250, 1326-1341, doi: 10.1093/gji/ggw082. [LINK]
  12. Li, Z., Z. Peng, Y. Ben-Zion, and F. Vernon (2015), Spatial variations of shear-wave anisotropy near the San Jacinto Fault Zone in southern California, J. Geophys. Res. Solid Earth, 120, 8334-8347, doi: 10.1002/2015JB012483. [LINK]
  13. Yang, W., Z. Peng, B. Wang, Z. Li, and S. Yuan (2015), Velocity contrast along the rupture zone of the 2010 Mw6.9 Yushu, China earthquake from systematic analysis of fault zone head waves, Earth Planet. Sci. Lett., 416, 91-97, doi: 10.1016/j.epsl.2015.01.043. [LINK]
  14. Yang, H., Z. Li, Z. Peng, Y. Ben-Zion, and F. Vernon (2014), Low velocity zones along the San Jacinto Fault, Southern California, from body waves recorded in dense linear arrays, J. Geophys. Res. Solid Earth, 119, 8976-8990, doi: 10.1002/2014JB011548. [LINK]
  15. Li, Z., H. Zhang, and Z. Peng (2014), Structure-controlled seismic anisotropy along the Karadere-Duzce branch of the north Anatolian fault revealed by shear-wave splitting tomography, Earth Planet. Sci. Lett., 391, 319-326, doi: 10.1016/j.epsl.2014.01.046. [LINK]
    SEG Conferences:
  1. Zhu, L., Y. Zhao, W. Li, E. Liu, J. H. McClellan, Z. Li, and Z. Peng (2017), Estimation of passive microseismic event location using random sampling based curve fitting, SEG Technical Program Expanded Abstracts 2017, pp. 2791-2796, doi: 10.1190/segam2017-17730445.1. [LINK]
  2. Li, Z., Z. Peng, L. Zhu, and J. H. McClellan (2017), High-resolution microseismic detection and location using Large-N arrays, 2017 Workshop: Microseismic Technologies and Applications, Hefei, China, 4-6 June 2017: pp. 59-63, doi: 10.1190/Microseismic2017-015. [LINK]
  3. Li, Z., Z. Peng, X. Meng, A. Inbal, Y. Xie, D. Hollis, and J. Ampuero (2015), Matched filter detection of microseismicity in Long Beach with a 5200-station Dense Array, SEG Technical Program Expanded Abstracts 2015, pp. 2615-2619, doi: 10.1190/segam2015-5924260.1. [LINK]
    Non-peer-reviewed:
  1. Bergen, K., T. Yang, and Z. Li (2019), Preface to the Focus Section on Machine Learning in Seismology. Seismological Research Letters, 90 (2A): 477–480. doi: https://doi.org/10.1785/0220190018 [LINK]

  Last updated by Zefeng Li at Caltech, Wed Mar 6 10:32:29 PST 2019