*Corresponding author, ^#Student advised/co-advised; Bold titles for recent highlights

16. Cheng, Y.*, Y. Ben-Zion, F. Brenguier, C. W. Johnson, Z. Li, P. Share, and F. Vernon (2020), An automated method for developing a catalog of small earthquakes using data of a dense seismic array and nearby stations, Seismo. Res. Lett., 91(5), 2862-2871, doi: 10.1785/0220200134. [LINK]

15. 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]

14. 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]

13. 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]

12. 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]

11. 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]

10. 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]

9. 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]

7. 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]

6. 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]

5. 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]

4. 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]

3. 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]

2. 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]

1. 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]

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]

2. Li, Z. (2017), Fault zone imaging and earthquake detection with dense seismic arrays, PhD Thesis at Georgia Institute of Technology. [LINK]