As machine learning algorithms can be prone to overfitting, the ability to them generalize for use in a real-time system for seismic event detection and location is critical. In this study, we focus on the temporal stability of a realtime automatic seismic catalog generator algorithm (Feature Weighted Beamforming, FWB) which has been applied on over 15 networks over a one year period in a production environment. We present detailed results from an induced seismic monitoring array over the Duvernay Formation (Duvernay Subscriber Array, DSA), as well as some higher level statistics on other seismic networks. The initial results from DSA in comparison to standard STA/LTA picking and associations show that FWB reduced the number of false positives by 75% without loss of sensitivity, it also reduced the average difference in the event location between automatic and manually picked solutions by 82%. Similar to DSA, for all networks which included a large variety of training data FWB demonstrated consistent detection of all real seismic events compared to a sensitive STA/LTA pick associator regarding system sensitivity and location accuracy. New clusters of seismic activity not seen during training are also correctly detected and located.


Article metrics loading...

Loading full text...

Full text loading...


  1. Domingos, P.
    [2012] A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87.
    [Google Scholar]
  2. Guyon, I., & Elisseeff, A.
    [2003] An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157–1182.
    [Google Scholar]
  3. Krischer, L., Megies, T., Barsch, R., Beyreuther, M., Lecocq, T., Caudron, C., ... and Wassermann, J.
    [2015] ObsPy: A bridge for seismology into the scientific Python ecosystem. Computational Science & Discovery, 8(1), 014003.
    [Google Scholar]
  4. Martín, A., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., … and Kudlur, M.
    [2016] Tensorflow: a system for large-scale machine learning. OSDI, vol. 16, 265–283.
    [Google Scholar]
  5. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... and Vanderplas, J.
    [2011] Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825–2830.
    [Google Scholar]
  6. Reynen, A., & Audet, P.
    [2017] Supervised machine learning on a network scale: Application to seismic event classification and detection. Geophysical Journal International, 210(3), 1394–1409.
    [Google Scholar]
  7. Reynen, A., Chambers, K., and Baturan, D.
    [2018] Automatic event detection and location using feature weighted beamforming. SEG Technical Program Expanded Abstracts 2018: 2206–2210 (Abstract).
    [Google Scholar]
  8. Riggelsen, C., and Ohrnberger, M.
    [2014] A machine learning approach for improving the detection capabilities at 3C seismic stations. Pure and Applied Geophysics, 171(3–5), 395–411.
    [Google Scholar]
  9. Ross, Z. E., Meier, M. A., Hauksson, E., and Heaton, T. H.
    [2018] Generalized Seismic Phase Detection with Deep Learning. arXiv preprint arXivs:1805.01075.
    [Google Scholar]
  10. Kaur, K., Wadhwa, M., and Park, E. K.
    [2013] Detection and identification of seismic P-Waves using Artificial Neural Networks. In Neural Networks (IJCNN), The 2013 International Joint Conference on Neural Networks: 1–6.
    [Google Scholar]
  11. Zhu, W., and Beroza, G. C.
    [2018] PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. arXiv preprint arXiv:1803.03211.
    [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error