Daniel Trugman

Summary

Daniel was born and raised in Los Alamos, New Mexico. After graduating from Los Alamos High School, he received a BS in Geophysics from Stanford and both an MS and a PhD in Earth Sciences from Scripps Institution of Oceanography at UC San Diego. Daniel returned to Los Alamos in 2018 as the Richard P. Feynman Postdoctoral Fellow and became an Assistant Professor in the Jackson School of Geosciences at UT Austin in 2020.

Daniel arrived at the University of Nevada, Reno in 2022, where his research is a perfect fit for the overarching mission of the Nevada Seismological Laboratory. In 2023, Daniel was awarded the Charles F. Richter Early Career Award from the Seismological Society of America and the Mousel-Feltner Award for Research Excellence from the College of Science.

Daniel loves to be outside, especially in the mountains, and spends his free time exploring the eastern Sierra by way of hiking, biking, rock climbing, skiing, and trail running.

Research interests

Dr. Trugman's 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. His research team at the University is broadly interested in leveraging concepts from big data and scientific machine learning alongside high-fidelity physical modeling in order to advance earthquake science.

Topics of particular interest include to Dr. Trugman's research team include:

• Nevada seismicity, tectonics, and earthquake sequences
• Earthquake source properties (magnitude, stress drop, and radiated energy estimates)
• Earthquake nucleation and rupture dynamics
• Stress transfer and earthquake triggering
• Earthquake early warning systems
• Ground motion prediction and hazard analysis
• Forensic seismology and nuclear monitoring

Courses taught

• GEOL 479/679: Python for Earth Sciences
• GE 479/679: Earthquake Engineering

Education

• Ph.D., Earth Sciences, University of California - San Diego, 2017
• M.S., Earth Sciences, University of California - San Diego, 2015
• B.S., Geophysics, Stanford University, 2013

Selected publications

• Trugman, D. T., and Y. Ben-Zion. Coherent variations in the productivity of earthquake sequences in California and Nevada, The Seismic Record, 3 (4): 322–331.
• Umlauft, J., C. W. Johnson, P. Roux, D. T. Trugman, A. Lecointre, A. Walpersdorf, U. Nanni, F. Gimbert, B. Rouet-Leduc, C. Hulbert, and P. A. Johnson (2023). Mapping glacier basal sliding applying machine learning. Journal of Geophysical Research – Earth’s Surface, 128, e2023JF007280.
• Trugman, D. T., W. H. Savran, C. J. Ruhl, and K. D. Smith (2023). Unraveling the evolution of an unusually active earthquake sequence near Sheldon, Nevada. Seismica 2 (2).
• Zhang, E., G. Catania, and D. T. Trugman (2023). Autoterm: A “big data” repository of glacier termini delineated using deep learning. The Cryosphere, 17, 3485-3503.
• Hua, J. , M. Wu, J. P. Mulholland, J. D. Neelin, V. C. Tsai, and D. T. Trugman (2023). Monitoring precipitation with a sense seismic nodal array. Nature Scientific Reports, 13 (11450).
• Bolton, D. C., D. Saffer, C. Marone, and D. T. Trugman (2023). Foreshock properties illuminate nucleation processes of slow and fast laboratory earthquakes. Nature Communications.
• Trugman, D. T., J. Brune, K. D. Smith, J. N. Louie, and G. M. Kent (2023). The rocks that did not fall: A multidisciplinary analysis of near-source ground motions from an active normal fault. AGU Advances, 4, e2023AV000885.
• Igonin, N., D. T. Trugman, K. Gonzalez, and D. W. Eaton (2023). Spectral characteristics of hydraulic-fracturing induced seismicity can distinguish between activation of faults and fractures. Seismological Research Letters, 1-16.
• Cochran, E. S., M. T. Page, N. J. van der Elst, Z. E. Ross, and D. T. Trugman (2023). Fault roughness at seismogenic depths and links to earthquake behavior. The Seismic Record, 3 (1): 37–47.
• Trugman, D. T., C. J. Chamberlain, A. Lomax, and A. Savvaidis (2022). GrowClust3D.jl: A Julia package for the relative relocation of earthquake hypocenters using 3D velocity models. Seismological Research Letters, 94 (1), 443-456.
• Chatterjee, A. C, N. Igonin, and D. T. Trugman (2022). A real-time and data-driven ground motion prediction framework for earthquake early warning. Bulletin of the Seismological Society of America, 113 (2): 676–689.
• Trugman, D. T., L. Fang, J. Ajo-Franklin, A. Nayak, and Z. Li (2022). Preface to the focus section on big data problems in seismology. Seismological Research Letters, 93 (5): 2423–2425.
• Shearer, P. M., R. A. Abercrombie, and D. T. 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, 127 (7), e2022JB024243.
• Bolton, D. C., S. Sharan, G. McLaskey, J. Riviére, P. Shokouhi, D. T. Trugman, and C. Marone (2022). The high-frequency signature of slow and fast laboratory earthquakes. Journal of Geophysical Research: Solid Earth, 127, e2022JB024170.
• Arrowsmith, S. J., D. T. Trugman, J. MacCarthy, K. J. Bergen, D. Lumley, and B. M. Magnani (2022). Big data seismology. Reviews of Geophysics, 60, e2021RG000769.
• 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.
• Corradini, M., I. W. McBrearty, D. T. Trugman, C. Satriano, P. A. Johnson, and P. Bernard (2022). Investigating the influence of earthquake source complexity on back-projection images using convolutional neural networks. Geophysical Journal International, ggac026.
• Saad, O. M., Y. Chen, D. T. Trugman, M. S. Soliman, L. Samy, A. Savvaidis, M. A. Khamis, A. G. 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.
• Wang, W., P. M. Shearer, J. Vidale, X. Xu, D. T. Trugman, and Y. Fialko (2022). Tidal modulation of seismicity at the Coso geothermal field. Earth and Planetary Science Letters, 579, 117335.
• Chu, S. X., V. C. Tsai, D. T. Trugman, and G. Hirth (2021). Fault interactions enhance high-frequency earthquake radiation. Geophysical Research Letters, 48, e2021GL095271.
• Abercrombie, R. E., D. T. Trugman, P. M. Shearer, X. Chen, J. Zhang, C. N. Pennington, J. L. Hardebeck, T. H. W. Goebel, and C. J. Ruhl (2021). Does earthquake stress drop increase with depth in the crust? Journal of Geophysical Research: Solid Earth, 126, e2021JB022314.
• Trugman, D. T., S. X. Chu, and V. C. Tsai (2021). Earthquake source complexity controls the frequency-dependence of near-source radiation patterns. Geophysical Research Letters, 48, e2021GL095022.
• Tsai, V. C., G. Hirth, D. T. Trugman, and S. X. Chu (2021). Impact versus frictional earthquake models for high-frequency radiation in complex fault zones. Journal of Geophysical Research: Solid Earth 126, e2021JB022313.
• Skoumal, R. J., and D. T. Trugman (2021). The proliferation of induced seismicity in the Permian Basin. Journal of Geophysical Research: Solid Earth, 126, e2021JB021921.
• Trugman, D. T., and A. Savvaidis (2021). Source spectral properties of earthquakes in the Delaware Basin of West Texas. Seismological Research Letters, 92 (4): 2477–2489.
• 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.
• Trugman, D. T., I. W. McBrearty, D. C. Bolton, R. A. Guyer, C. Marone, and P. A. Johnson (2020). The spatiotemporal evolution of granular microslip precursors to laboratory earthquakes. Geophysical Research Letters, 47 (16), e2020GL088404.
• Ross, Z. E., E. S. Cochran, D. T. Trugman, and J. D. Smith (2020). 3D fault architecture controls the dynamism of earthquake swarms. Science, 368 (6497), 1357–1361.
• 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.
• Trugman, D. T., Z. E. Ross, and P. A. Johnson (2020). Imaging stress and faulting complexity through earthquake waveform similarity. Geophysical Research Letters, 47 (1), e2019GL085888.
• Ross, Z. E., D. T. Trugman, K. Azizzadenesheli, and A. Anandkumar (2020). Directivity modes of earthquake populations with unsupervised learning. Journal of Geophysical Research: Solid Earth, 125 (2), e2019JB018299.
• Qin, Y., X. Chen, J. I. Walter, J. Haffener, D. T. Trugman, B. M. 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.
• Trugman, D. T., and Z. E. Ross (2019). Pervasive foreshock activity across Southern California. Geophysical Research Letters, 46 (15), 8772-8781.
• Ross, Z. E., D. T. Trugman, Hauksson, E., and Shearer, P. M. (2019). Searching for hidden earthquakes in Southern California. Science, 364(6442), 767–771.
• Trugman, D. T., M. T. Page, S. E. Minson, and E. S. Cochran (2019). Peak ground displacement saturates exactly when expected: Implications for earthquake early warning. Journal of Geophysical Research: Solid Earth, 124 (5), 4642– 4653.
• Shearer, P. M., R. A. Abercrombie, D. T. 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.
• Kong, Q., D. T. Trugman, Z. E. Ross, M. J. Bianco, B. J. Meade, and P. Gerstoft (2019). Machine learning in seismology – Turning data into insights. Seismological Research Letters, 90(1), 3-14.
• Koper, K. D., K. L. Pankow, J. C. Pechmann, J. M. Hale, R. Burlacau, W. L. Yeck, H. M. Benz, R. B. Hermann, D. T. Trugman, and P. M. Shearer (2018). Afterslip enhanced aftershock activity during the 2017 earthquake sequence near Sulphur Peak, Idaho. Geophysical Research Letters, 45, 5352–5361.
• Trugman, D. T., and P. M. 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.
• Trugman, D. T., S. L. Dougherty, E. S. Cochran, and P. M. Shearer (2017). Source spectral properties of small to moderate earthquakes in Southern Kansas. Journal of Geophysical Research: Solid Earth, 122 (10), 8021–8034.
• Trugman, D. T., and P. M. 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.
• Trugman, D. T., and P. M. 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.