Professional Education


  • Doctor of Philosophy, Stanford University, GEOPH-PHD (2017)
  • Master of Science, Uppsala Universitet, Engineering physics (2011)

All Publications


  • Treatment of the polar coordinate singularity in axisymmetric wave propagation using high-order summation-by-parts operators on a staggered grid COMPUTERS & FLUIDS Prochnow, B., O'Reilly, O., Dunham, E. M., Petersson, N. A. 2017; 149: 138-149
  • Discretizing singular point sources in hyperbolic wave propagation problems JOURNAL OF COMPUTATIONAL PHYSICS Petersson, N. A., O'Reilly, O., Sjoegreen, B., Bydlon, S. 2016; 321: 532-555
  • 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

  • Simulation of Earthquake Rupture Dynamics in Complex Geometries Using Coupled Finite Difference and Finite Volume Methods COMMUNICATIONS IN COMPUTATIONAL PHYSICS O'Reilly, O., Nordstrom, J., Kozdon, J. E., Dunham, E. M. 2015; 17 (2): 337-370