Bio


My name is Clara Yoon, and I am a PhD candidate in earthquake seismology at Stanford University, advised by Prof. Greg Beroza and working closely with Prof. Bill Ellsworth.

I have a unique, diverse combination of skills in geophysics, seismology, radar science, and software development that enables me to successfully approach interdisciplinary scientific problems and develop robust technical solutions.

I am currently seeking employment as I expect to receive my PhD in geophysics from Stanford University in April 2018. I prefer to relocate to the Los Angeles area, although I am considering jobs anywhere in California.

Please visit my professional website for more information about my research and skills: https://claraeyoon.wordpress.com

Honors & Awards


  • 3rd Place Oral Presentation, Stanford School of Earth, Energy, and Environmental Sciences Research Review (2015)
  • Joshua L. Soske Memorial Fellowship, Stanford School of Earth Sciences (2015)
  • Best Oral Presentation, Stanford School of Earth Sciences Research Review (2014)
  • Chevron Fellow, Stanford Graduate Fellowship (2013-present)

Professional Affiliations and Activities


  • Member, Seismological Society of America (2012 - Present)
  • Member, Southern California Earthquake Center (2012 - Present)
  • Member, American Geophysical Union (2008 - Present)
  • Member, Institute of Electrical and Electronics Engineers (2008 - 2012)

Education & Certifications


  • M.S., Stanford University, Geophysics (2015)
  • B.S., University of California Los Angeles, Physics, Minor in mathematics (2006)

Stanford Advisors


Projects


  • Data mining algorithms for earthquake detection, Department of Geophysics, Stanford University (2013 - 2015)

    •Developed a prototype MATLAB/Python/C++ application, based on music recognition technology, to detect earthquakes with similar waveforms in continuous seismic data
    •Detected low-magnitude, uncataloged earthquakes on the Calaveras Fault in central California 140 times faster than a reference autocorrelation algorithm

    Location

    Stanford, CA

    Collaborators

    • Gregory Beroza, Wayne Loel Professor, Geophysics
    • Karianne Bergen, Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2011, School of Engineering
    • Ossian O'Reilly, Ph.D. Student in Geophysics, admitted Autumn 2011, School of Earth, Energy & Environmental Sciences
  • Big seismic data analytics: blind search for similar earthquakes, Stanford University (2017 - Present)

    •Developed optimized, parallel Python/C++ software to detect earthquakes with similar waveforms in large continuous data sets (>10 years duration), in collaboration with Stanford Computer Science researchers
    •Identified uncataloged small earthquakes in coastal central California from 2007 to 2017

    Location

    Stanford, CA

    Collaborators

    • Karianne Bergen, Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2011, School of Engineering
    • Hashem Elezabi, Vice Provost for Undergraduate Education
    • Peter Bailis, Assistant Professor of Computer Science, Department of Computer Science
    • Gregory Beroza, Wayne Loel Professor, Geophysics
  • Induced seismicity, Stanford University (2015 - 2017)

    •Detected, located, and characterized microearthquakes in first 3 months of the Guy-Greenbrier, Arkansas, earthquake swarm
    •Analyzed well data from Arkansas Oil and Gas Database; identified earthquakes induced by hydraulic fracturing stimulation and deep wastewater injection

    Location

    Stanford, CA

    Collaborators

    • Yihe Huang, Assistant Professor, Earth and Environmental Sciences, University of Michigan
    • William Ellsworth, Professor (Research) of Geophysics, Geophysics
    • Gregory Beroza, Wayne Loel Professor, Geophysics
  • Foreshocks and mainshock nucleation, Stanford University (2017 - Present)

    •Detected, located, and computed source parameters for foreshocks of the 1999 Mw 7.1 Hector Mine, California earthquake; analyzed relationship between foreshocks and mainshock

    Location

    Stanford, CA

    Collaborators

  • Repeating earthquake and aftershock detection, Stanford University (2017 - Present)

    •Detected repeating earthquakes and aftershocks of the 2012 Mw 7.4 Ometepec, Mexico earthquake to assess changes in deep aseismic slip

    Location

    Stanford, CA

    Collaborators

    • Luis Dominguez, Assistant Professor, UNAM ENES Morelia
  • Aftershock detection and phase picking, Stanford University (2017 - Present)

    •Automatically detected and picked phases on aftershock waveforms of the 2008 Wenchuan earthquake for the SeismOlympics data science competition

    Location

    Stanford, CA

    Collaborators

    • seyed mostafa mousavi, Postdoctoral Research Fellow, Geophysics, School of Earth, Energy & Environmental Sciences
  • Radar Interferometry Applied to Induced Seismicity, Department of Geophysics, Stanford University (2013 - 2016)

    •Performed comprehensive search for crustal deformation signals in InSAR data associated with potentially induced seismicity in the United States
    •Conducted multi-year InSAR time series analysis with Python/MATLAB/shell scripts to estimate surface deformation from deep wastewater injection near Oklahoma City during 2011-2014

    Location

    Stanford, CA

    Collaborators

    • Howard Zebker, Professor of Electrical Engineering and of Geophysics, Stanford University
    • Sang-Ho Yun, Radar Engineer, NASA-JPL

Lab Affiliations


Work Experience


  • Teaching Assistant: Imaging Radar and Applications, Department of Electrical Engineering/Geophysics, Stanford University (1/2015 - 3/2015)

    •Presented lectures on advanced synthetic aperture radar imaging methods
    •Prepared solutions for and graded homework and exams
    •Maintained course website and online message board for student questions
    •Held office hours and exam review session

    Location

    Stanford, CA

  • Teaching Assistant: Introduction to Seismology, Department of Geophysics, Stanford University (9/22/2014 - 12/19/2014)

    •Presented lecture on signal processing techniques in seismology
    •Held office hours, graded homework, and responded to student questions

    Location

    Stanford, CA

  • Staff Scientist, Arete Associates (2006 - 2012)

    •Lead scientific programmer on research and development projects for remote sensing systems; performed simulation and modeling, data analysis, algorithm development, and software implementation in C++ and Fortran 90
    •Implemented, tested, and delivered a C++ computational geometry optimization software package
    •Extensively debugged deliverable software; developed unit and regression tests
    •Generated algorithm documents with conceptual and mathematical descriptions
    •Wrote three technical papers summarizing data analysis procedures and results
    •Presented technical briefings to customers at review meetings
    •Designed, developed and implemented an interpolation algorithm to map high-resolution synthetic aperture radar data, which improved image resolution, added processing flexibility, and reduced computational cost
    •Debugged, refined, and analyzed physics-based algorithms used to process optical image data, which enhanced feature detection performance and reduced run-time
    •Developed and implemented noise reduction algorithms, then analyzed their performance on large data sets
    •Experienced with image processing, signal processing, digital filtering, numerical analysis and iterative algorithms, probability and statistics

    Location

    Northridge, CA

All Publications


  • Scalable Similarity Search in Seismology: A New Approach to Large-Scale Earthquake Detection Proceedings, Similarity Search and Applications: 9th International Conference, SISAP 2016 Bergen, K., Yoon, C., Beroza, G. C.
  • Seismicity During the Initial Stages of the Guy-Greenbrier, Arkansas, Earthquake Sequence Journal of Geophysical Research – Solid Earth (Submitted) Yoon, C. E., Huang, Y., Ellsworth, W. L., Beroza, G. C. 2017
  • Earthquake detection through computationally efficient similarity search. Science advances Yoon, C. E., O'Reilly, O., Bergen, K. J., Beroza, G. C. 2015; 1 (11)

    Abstract

    Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection-identification of seismic events in continuous data-is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection methods. Our method, called Fingerprint And Similarity Thresholding (FAST), can analyze a week of continuous seismic waveform data in less than 2 hours, or 140 times faster than autocorrelation. FAST adapts a data mining algorithm, originally designed to identify similar audio clips within large databases; it first creates compact "fingerprints" of waveforms by extracting key discriminative features, then groups similar fingerprints together within a database to facilitate fast, scalable search for similar fingerprint pairs, and finally generates a list of earthquake detections. FAST detected most (21 of 24) cataloged earthquakes and 68 uncataloged earthquakes in 1 week of continuous data from a station located near the Calaveras Fault in central California, achieving detection performance comparable to that of autocorrelation, with some additional false detections. FAST is expected to realize its full potential when applied to extremely long duration data sets over a distributed network of seismic stations. The widespread application of FAST has the potential to aid in the discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes.

    View details for DOI 10.1126/sciadv.1501057

    View details for PubMedID 26665176

    View details for PubMedCentralID PMC4672764