Daniel Trugman

Daniel  Trugman
Assistant Professor, Department of Geological Sciences, Jackson School of Geosciences

Office: JGB 4.220C
Mailcode: C1160

My research focuses on developing and applying new techniques to analyze large seismic datasets in order to better understand earthquake rupture processes and their links to earthquake hazards. I am broadly interested in leveraging concepts from big data and scientific machine learning to advance earthquake science.

Topics of particular interest include:
- Earthquake source properties (stress drop and radiated energy estimates)
- Earthquake nucleation and rupture dynamics
- Stress transfer and earthquake triggering
- Induced seismicity and hazard mitigation
- Earthquake early warning
- Ground motion prediction
- Forensic seismology and nuclear monitoring

Find out more about my research and that of my team at the Earthquake Science Lab webpage.

Interested in joining our group? Prospective graduate students can learn more about the application process at the JSG admissions webpage.

G. Moses and Carolyn G. Knebel Distinguished Teaching Award - Department of Geological Sciences, UT Austin (2021)

Top Five Downloaded Paper in 2020: Bulletin of Seismological Society of America - Seismological Society of America (2021)

Top Downloaded Paper Award: Geophysical Research Letters - American Geophysical Union (2020)

Editors' Citation for Excellence in Refereeing: Geophysical Research Letters - American Geophysical Union (2020)

Geophysical Research Letters Editor's Highlight - American Geophysical Union (2019)

Top 50 Science Story of 2019 - Discover Magazine (2019)

HPC Innovation Excellence Award - Hyperion Research (2019)

Richard P. Feynman Postdoctoral Fellowship - Los Alamos National Laboratory (2018)

Outstanding Student Presentation Award - Seismological Society of America (2017)

Paul G. Silver Young Scholar Research Enhancement Award - Scripps Institution of Oceanography (2016)

Achievement Reward for College Scientists (ARCS) Scholarship - ARCS Foundation (2016)

National Science Foundation Graduate Research Fellowship - NSF (2014)

Diedrick Scholar - California Federation of Mineralogical Society (2013)

Dean's Award for Undergraduate Academic Achievement - Stanford University, School of Earth Sciences (2013)

David M. Kennedy Prize for Outstanding Undergraduate Honors Thesis - Stanford University (2013)

Hoefer Prize for Excellence in Undergraduate Writing - Stanford University (2013)

Firestone Medal for Excellence in Undergraduate Research - Stanford University (2013)

National Merit Scholar - NMSC (2009)

Platinum Scholar - Los Alamos National Laboratory (2009)

J. Robert Oppenheimer Scholar - Los Alamos National Laboratory (2009)

Search Committee Representative, Tenure-Track Position in Structural Geology, Department of Geological Sciences, UT Austin (2021)

Search Committee Representative, TexNet Seismologist Position, Bureau of Economic Geology, UT Austin (2021)

Committee Member, LDE Annual Performance Review, Department of Geological Sciences, UT Austin (2021 - Present)

Panel Reviewer, Office of Science, United States Department of Energy (2021)

Committee Member and Co-Writer, AGU Bridge Program Ad Hoc Committee, Department of Geological Sciences, UT Austin (2020 - Present)

Co-organizer, Machine Learning in Geoscience Certificate Program, Department of Geological Sciences, UT Austin (2020 - Present)

Panel Reviewer, Office of Science, United States Department of Energy (2019)

Participant, Earthquake Early Warning Scientific Forum, ShakeAlert System (2019 - Present)

Organizing Committee Member, Machine Learning in Solid Earth Science Conference, Los Alamos National Laboratory (2019 - 2020)

Head Organizer, Earth and Environmental Sciences Division Seminar Series on Machine Learning, Los Alamos National Laboratory (2019)

Panel Reviewer, USGS National Earthquake Hazards Reduction Program, United States Department of Interior (2019)

Co-Organizer, Earth and Environmental Sciences Division Brown Bag Seminar Series, Los Alamos National Laboratory (2019 - 2020)

Instructor, Machine Learning in Seismology Workshop, Seismological Society of America Annual Meeting (2019 - Present)

Advisory Committee Member, Los Alamos National Laboratory Employee's Scholarship Fund, Los Alamos National Laboratory (2018 - 2020)

Journal Peer Reviewer, Nature, Nature Communications, AGU Advances, Geophysical Research Letters, Journal of Geophysical Research, Bulletin of the Seismological Society of America, Seismological Research Letters, Reviews of Geophysics, Earth and Planetary Science Letters, Geophysical Journal International, (2015 - Present)

Academic Advisory Participant, Uniform California Earthquake Rupture Forecast (UCERF) Planning Committee, US Geological Survey (2015 - 2019)

Postdocs

Nadine Igonin
Nadine Igonin is a postdoctoral fellow at the University of Texas in Austin. She is working on analysis of injection induced seismicity in Texas, maximum magnitude relationships and earthquake source spectra. More information about her research can be found at www.toc2me.com


Graduate Students

Michelle Tebolt , Ph.D., expected 2024 (Committee Member)

Nam P Pham , Ph.D., expected 2023 (Committee Member)

Avigyan Chatterjee (Supervisor)

Vivian Rosas (Supervisor)

Ruide Ao, M.S., 2021 (Co-supervisor)
Department of Geological Sciences, University of Texas at Austin
Systematic comparison of different machine learning based earthquake detection methods


Space-time variations in earthquake waveform similarity: Implications for stress heterogeneity and faulting complexity, Seismological Society of America Annual Meeting, (2021)

Machine learning in seismology: A fireside chat, Seismological Society of America Annual Meeting, (2021)

Rupture determinism and magnitude saturation: Practical implications for the ShakeAlert system, ShakeAlert Research Workshop, (2021)

Source spectral properties of earthquakes in the Delaware Basin of west Texas, TexNet Seismology Research Seminar, Austin, TX (2021)

Earthquake waveform similarity as a tool to image stress and fault complexity: Application to the 2019 Ridgecrest earthquake sequence, Seismological Society of America Annual Meeting, Albuquerque, NM (Postponed) (2020)

New insights into earthquake rupture processes from high-resolution California datasets, University of Utah SeismoTea Seminar, Salt Lake City, Utah (2020)

Imaging stress and faulting complexity through earthquake waveform similarity, Southern California Earthquake Center Annual Meeting, Palm Springs, CA (2020)

Waveform similarity and earthquake stress drop: What can we learn from the source properties of small earthquakes in the Ridgecrest sequence?, Physics-based Earthquake Forecasting Community Seminar, UK Research Institute (2020)

What can small earthquakes tell us about large earthquake ruptures? Insights from the July 2019 Ridgecrest, CA sequence, Lithosphere and Deep Earth Seminar Series, Austin, TX (2020)

Do large and small earthquakes start alike? Rupture determinism and earthquake early warning, Los Alamos National Laboratory Earth Science Cafe, Los Alamos, NM (2019)

Big data, small earthquakes, Triad Science, Technology, and Energy Review, Santa Fe, NM (2019)

Big data, small earthquakes: Insights into earthquake nucleation, Scripps Institution of Oceanography, La Jolla, CA (2019)

Earthquake nucleation: Observation and applications from megaquakes in Japan to microforeshocks in California, Caltech Seismolab Seminar, Pasadena, CA (2019)

New perspectives on earthquake nucleation from megaquakes in Japan and microforeshocks in California, Harvard Department of Earth and Planetary Sciences Seminar, Cambridge, MA (2019)

New perspectives on earthquake nucleation from megaquakes in Japan and microforeshocks in California, UT Austin Jackson School of Geosciences DeFord Lecture, Austin, TX (2019)

Characterizing earthquake hazards and source dynamics using machine learning, MIT Earth Research Laboratory Seminar, Cambridge, MA (2018)

Machine learning applications to earthquake source characterization and hazard analysis, Caltech Seismolab Seminar, Pasadena, CA (2018)

Machine learning applications to earthquake source characterization and hazard analysis, US Geological Survey Earthquake Science Center, Menlo Park, CA (2018)

Earthquake stress drop and peak ground motion: A machine learning perspective, AGU Fall Meeting, Washington DC (2018)

Earthquake stress drop and source parameter scaling in Southern Kansas, USGS Earthquake Science Center, Menlo Park, CA (2017)

Brittle-ductile friction model: Tremor, triggering and LFE broadcasts, Penn State University, State College, PA (2013)

Graduate Positions

Student Opportunities
I am always interested in adding motivated new students to my Earthquake Science research team in the Jackson School. For prospective graduate students, please review the application guidelines and expectations listed on the Jackson School website (see orange link above). We do not accept "off track" admissions in the Jackson School, so the standard Fall application season is your best bet. I strongly encourage prospective students to reach out to me via email during this time with your CV and research interests. I highly value diversity in thought and experience, and students from underrepresented groups are strongly encouraged to apply.

 

External Photo Galleries


Climbing and Hiking
A random assortment of climbing photos, featuring The Sierra Nevada, The Grand Teton, and Cotopaxi (Ecuador).

GrowClust Software Repository
GitHub repository for the free GrowClust software, a relative relocation code for earthquake hypocenters.

Earthquake Science Research Group
Find out more about my research group in the Jackson School. Our research focuses on better understanding the physics of the earthquake rupture process in light of new data, and on using this understanding to improve seismic hazard analysis.