Daniel Trugman | Department of Geological Sciences and Engineering


My research focuses on developing and applying new techniques to analyze large datasets of seismic waveforms in order to better understand earthquake rupture processes and their relation to seismic hazards. I am broadly interested in leveraging concepts from big data and scientific machine learning alongside high-fidelity physical modeling in order to advance earthquake science.

My research team at UNR works on a wide range of projects across earthquake science. Topics of particular interest include:

  • Earthquake source properties (magnitude, stress drop, and radiated energy estimates)
  • Earthquake nucleation and rupture dynamics
  • Stress transfer and earthquake triggering
  • Earthquake early warning
  • Ground motion prediction
  • Forensic seismology and nuclear monitoring
  • Earthquake geodesy, strain accumulation, in relation to hazard


Ph.D., Earth Sciences, University of California-San Diego, 2017
MS, Earth Sciences, University of California-San Diego, 2015
BS, Geophysics, Stanford University, 2013

selected publications

  • Shearer, PM, RA Abercrombie, and DT Trugman (2022). Improved stress drop estimates for M 1.5 to 4 earthquakes in Southern California from 1996 to 2019. Journal of Geophysical Research: Solid Earth, e2022JB024243, doi: 10.1029/2022JB024243.
  • Bolton, DC, S. Sharan, G. McLaskey, J. Riviere, P. Shokouhi, DT Trugman, and C. Marone (2022). The high-frequency signature of slow and fast laboratory earthquakes. Journal of Geophysical Research: Solid Earth, e2022JB024170, doi: 10.1029/2022JB024170.
  • Arrowsmith, SJ, DT Trugman, J. MacCarthy, KJ Bergen, D. Lumley, and BM Magnani (2022). Big data seismology. Reviews of Geophysics, 60, e2021RG000769, doi: 10.1029/2021RG000769.
  • Trugman, D. T. (2022). Resolving differences in the rupture properties of M5 earthquakes in California using Bayesian source spectral analysis. Journal of Geophysical Research: Solid Earth, 127 (4), e2021JB023526, doi: 10.1029/2021JB023526.
  • Corradini, M., IW McBrearty, DT Trugman, C. Satriano, PA Johnson, and P. Bernard (2022). Investigating the influence of earthquake source complexity on back-projection images using convolutional neural networks. Geophysical Journal International, ggac026, doi: 10.1093/gji/ggac026.
  • Saad, OM, Y. Chen, DT Trugman, MS Soliman, L. Samy, A. Savvaidis, MA Khamis, AG Hafez, S. Fomel, and Y. Chen (2022). Machine learning for fast and reliable source-location estimation in earthquake early warning. IEEE Transactions on Geoscience and Remote Sensing, 19, pp.1-5, doi: 10.1109/LGRS.2022.3142714.
  • Wang, W., PM Shearer, J. Vidale, X. Xu, DT Trugman, and Y. Fialko (2022). Tidal modulation of seismicity at the Coso geothermal field. Earth and Planetary Science Letters, 579, 117335, doi: 10.1016/j.epsl.2021.117335.
  • Chu, SX, VC Tsai, DT Trugman, and G. Hirth (2021). Fault interactions enhance high-frequency earthquake radiation. Geophysical Research Letters, 48, e2021GL095271, doi: 10.1029/2021GL095271.
  • Abercrombie, RE, DT Trugman, PM Shearer, X. Chen, J. Zhang, CN Pennington, JL Hardebeck, THW Goebel, and CJ Ruhl (2021). Does earthquake stress drop increase with depth in the crust? Journal of Geophysical Research: Solid Earth, 126, e2021JB022314, doi: 10.1029/2021JB022314.
  • Trugman, DT, SX Chu, and VC Tsai (2021). Earthquake source complexity controls the frequency-dependence of near-source radiation patterns. Geophysical Research Letters, 48, e2021GL095022, doi: 10.1029/2021GL095022.
  • Tsai, VC, G. Hirth, DT Trugman, and SX Chu (2021). Impact versus frictional earthquake models for high-frequency radiation in complex fault zones. Journal of Geophysical Research: Solid Earth 126, e2021JB022313, doi: 10.1029/2021JB022313.
  • Skoumal, RJ, and DT Trugman (2021). The proliferation of induced seismicity in the Permian Basin. Journal of Geophysical Research: Solid Earth, 126, e2021JB021921, doi: 10.1029/2021JB021921.
  • Trugman, DT, and A. Savvaidis (2021). Source spectral properties of earthquakes in the Delaware Basin of West Texas. Seismological Research Letters, 92 (4): 2477–2489, doi: 10.1785/0220200461.
  • Wang, T., D. T. Trugman, and Y. Lin (2021). SeismoGen: Seismic waveform synthesis using generative adversarial networks. Journal of Geophysical Research: Solid Earth, 126, e2020JB020077, doi: 10.1029/2020JB020077.
  • Trugman, DT, IW McBrearty, DC Bolton, RA Guyer, C. Marone, and PA Johnson (2020). The spatiotemporal evolution of granular microslip precursors to laboratory earthquakes. Geophysical Research Letters, 47 (16), e2020GL088404, doi: 10.1029/2020GL088404.
  • Ross, ZE, ES Cochran, DT Trugman, and JD Smith (2020). 3D fault architecture controls the dynamism of earthquake swarms. Science, 368 (6497), 1357–1361, doi: 10.1126/science.abb0779.
  • Trugman, D. T. (2020). Stress drop and source scaling of the 2019 Ridgecrest, California, earthquake sequence. Bulletin of the Seismological Society of America, 110 (4), 1859-1871, doi: 10.1785/012020009.
  • Trugman, DT, Z.E. Ross, and PA Johnson (2020). Imaging stress and faulting complexity through earthquake waveform similarity. Geophysical Research Letters, 47 (1), e2019GL085888, doi: 10.1029/2019GL085888.
  • Ross, ZE, DT Trugman, K. Azizzadenesheli, and A. Anandkumar (2020). Directivity modes of earthquake populations with unsupervised learning. Journal of Geophysical Research: Solid Earth, 125 (2), e2019JB018299, doi: 10.1029/2019JB018299.
  • Qin, Y., X. Chen, JI Walter, J. Haffener, DT Trugman, BM Carpenter, M. Weingarten, and F. Kolawole (2019). Deciphering the stress state of seismogenic faults in Oklahoma and Southern Kansas based on an improved stress map. Journal of Geophysical Research: Solid Earth, 124, 12920–12934, doi: 10.1029/2019JB018377.
  • Trugman, D. T., and Z. E. Ross (2019). Pervasive foreshock activity across Southern California. Geophysical Research Letters, 46 (15), 8772-8781, doi: 10.1029/2019GL083725.
  • Ross, ZE, DT Trugman, Hauksson, E., and Shearer, PM (2019). Searching for hidden earthquakes in Southern California. Science, 364(6442), 767–771, doi: 10.1126/science.aaw6888.
  • Trugman, DT, MT Page, SE Minson, and ES Cochran (2019). Peak ground displacement saturates exactly when expected: Implications for earthquake early warning. Journal of Geophysical Research: Solid Earth, 124 (5), 4642–4653, doi: 10.1029/2018JB017093.
  • Shearer, PM, RA Abercrombie, DT Trugman, and W. Wang (2019). Comparing EGF methods for estimating corner frequency and stress drop from P-wave spectra. Journal of Geophysical Research: Solid Earth, 124 (4), 3966-3986, doi: 10.1029/2018JB016957.
  • Kong, Q., DT Trugman, ZE Ross, MJ Bianco, BJ Meade, and P. Gerstoft (2019). Machine learning in seismology – Turning data into insights. Seismological Research Letters, 90(1), 3-14, doi: 10.1785/0220180259.
  • Koper, KD, KL Pankow, JC Pechmann, JM Hale, R. Burlacau, WL Yeck, HM Benz, RB Hermann, DT Trugman, and PM Shearer (2018). Afterslip enhanced aftershock activity during the 2017 earthquake sequence near Sulfur Peak, Idaho. Geophysical Research Letters, 45, 5352–5361, doi: 10.1029/2018GL078196.
  • Trugman, DT, and PM Shearer (2018). Strong correlation between stress drop and peak ground acceleration for recent M1-M4 seismicity in the San Francisco Bay Area. Bulletin of the Seismological Society of America, 108 (2), 929-945, doi: 10.1785/0120170245.
  • Trugman, DT, SL Dougherty, ES Cochran, and PM Shearer (2017). Source spectral properties of small to moderate earthquakes in Southern Kansas. Journal of Geophysical Research: Solid Earth, 122 (10), 8021–8034, doi: 10.1002/2017JB014649.
  • Trugman, DT, and PM Shearer (2017). Application of an improved spectral decomposition method to examine earthquake source scaling in Southern California. Journal of Geophysical Research: Solid Earth, 122 (4), 2890–2910, doi: 10.1002/2017JB013971.
  • Trugman, DT, and PM Shearer (2017). GrowClust: A hierarchical clustering algorithm for relative earthquake relocation, with application to the Spanish Springs and Sheldon, Nevada, earthquake sequences. Seismological Research Letters, 88 (2A), 379–391, doi: 10.1785/0220160188.

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