Figure 4 . Additional evaluation of the ANN model after training. Panels (a) and (b) compare the ANN model against three analysts A, B, and C using a subset of 3000 seismograms from Dataset DA and DB. Note the time spent by the ANN model in (b) includes the entire processing workflow from raw seismograms to quality labels. Panels (c) and (d) show F1 and number of ML model labeled seismograms as a function of probability threshold using dataset 3. The sample seismogram in (e) was rejected by Analyst B and accepted by Analyst A, Analyst C, and the ANN model. The vertical line indicates the origin time of the seismic event. The gray box represents the expected arrival time window of surface waves defined by a minimum group velocity of 2.5 km/s and a maximum of 5 km/s.
Acknowledgments and Data
This work was supported by the U.S. Department of Energy (DOE), Office of Fossil Energy, Carbon Storage Program through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US DOE. The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan, last accessed in January 2021). We thank helpful discussions with Kipton Barros, Singanallur Venkatakrishnan, and Derek Rose. The authors declare that there is no conflict of interest regarding the publication of this article.
The authors thank the developers of GMT version 5.4.4 (Paul Wessel et al., 2013) and version 6.1.1 (P. Wessel et al., 2019), Obspy version 1.2.2 (Beyreuther et al., 2010; Krischer et al., 2015; Megies et al., 2011), Numpy (Van Der Walt et al., 2011), Matplotlib version 3.4.2 (Hunter, 2007), Scikit-learn version 0.23.2 (Pedregosa et al., 2011), Keras version 2.4.3 (https://keras.io/, last accessed in January 2021), and Google Earth (https://www.google.com/earth/, last accessed in January 2021). The facilities of the Incorporated Research Institutions for Seismology (IRIS) Data Services, and specifically the IRIS Data Management Center (https://ds.iris.edu/ds/nodes/dmc/, last accessed in January 2021), were used for access to waveforms and related metadata required for waveform data. See Table S1 for a full list of seismic networks used in this study. We thank United States Geological Survey for making the ComCat catalog (https://earthquake.usgs.gov/earthquakes/search/, last accessed in January 2021) openly available.
References
Ammon, C. J. (2005). Rupture Process of the 2004 Sumatra-Andaman Earthquake. Science , 308 (5725), 1133–1139. https://doi.org/10.1126/science.1112260
Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., & Wassermann, J. (2010). ObsPy: A Python Toolbox for Seismology.Seismological Research Letters , 81 (3), 530–533. https://doi.org/10.1785/gssrl.81.3.530
Bianco, M. J., & Gerstoft, P. (2018). Travel Time Tomography With Adaptive Dictionaries. IEEE Transactions on Computational Imaging , 4 (4), 499–511. https://doi.org/10.1109/TCI.2018.2862644
Bird, P. (2003). An updated digital model of plate boundaries.Geochemistry, Geophysics, Geosystems , 4 (3), 1027. https://doi.org/10.1029/2001GC000252
Breiman, L. (2001). Random Forest. Machine Learning , 45 , 5–32. https://doi.org/10.1023/A:1010933404324
Chai, C., Ammon, C. J., Maceira, M., & Herrmann, R. B. (2018). Interactive Visualization of Complex Seismic Data and Models Using Bokeh. Seismological Research Letters , 89 (2A), 668–676. https://doi.org/10.1785/0220170132
Chai, C., Ammon, C. J., & Cleveland, K. M. (2019). Aftershocks of the 2012 Off-Coast of Sumatra Earthquake Sequence. Tectonophysics ,763 (April), 61–72. https://doi.org/10.1016/j.tecto.2019.04.028
Chai, C., Maceira, M., Santos‐Villalobos, H. J., Venkatakrishnan, S. V., Schoenball, M., Zhu, W., et al. (2020). Using a Deep Neural Network and Transfer Learning to Bridge Scales for Seismic Phase Picking.Geophysical Research Letters , 47 (16), e2020GL088651. https://doi.org/10.1029/2020GL088651
Cleveland, K. M., & Ammon, C. J. (2013). Precise relative earthquake location using surface waves. Journal of Geophysical Research: Solid Earth . https://doi.org/10.1002/jgrb.50146
Cleveland, K. M., & Ammon, C. J. (2015). Precise Relative Earthquake Magnitudes from Cross Correlation. Bulletin of the Seismological Society of America , 105 (3), 1792–1796. https://doi.org/10.1785/0120140329
Cleveland, K. M., VanDeMark, T. F., & Ammon, C. J. (2015). Precise relative locations for earthquakes in the northeast Pacific region.Journal of Geophysical Research: Solid Earth , 120 (10), 6960–6976. https://doi.org/10.1002/2015JB012161
Cleveland, K. M., Ammon, C. J., & Kintner, J. (2018). Relocation of Light and Moderate-Magnitude ( M 4-6) Seismicity Along the Central Mid-Atlantic. Geochemistry, Geophysics, Geosystems , 19 (8), 2843–2856. https://doi.org/10.1029/2018GC007573
Ekström, G., Nettles, M., & Dziewoński, A. M. (2012). The global CMT project 2004–2010: Centroid-moment tensors for 13,017 earthquakes.Physics of the Earth and Planetary Interiors ,200201 , 1–9. https://doi.org/10.1016/j.pepi.2012.04.002
Ekström, Göran. (2011). A global model of Love and Rayleigh surface wave dispersion and anisotropy, 25-250 s. Geophysical Journal International , 187 (3), 1668–1686. https://doi.org/10.1111/j.1365-246X.2011.05225.x
Herrmann, R. B., Ammon, C. J., Benz, H. M., Aziz-Zanjani, A., & Boschelli, J. (2021). Short-Period Surface-Wave Tomography in the Continental United States-A Resource for Research. Seismological Research Letters . https://doi.org/10.1785/0220200462
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013).Applied logistic regression (3rd ed.). Hoboken, New Jersey: John Wiley & Sons, Inc.
Howe, M., Ekström, G., & Nettles, M. (2019). Improving relative earthquake locations using surface-wave source corrections.Geophysical Journal International , 219 (1), 297–312. https://doi.org/10.1093/gji/ggz291
Hunter, J. D. (2007). Matplotlib: a 2D graphics environment.Computing in Science & Engineering , 9 (3), 90–95. https://doi.org/10.1109/MCSE.2007.55
Jain, A. K., Jianchang Mao, & Mohiuddin, K. M. (1996). Artificial neural networks: a tutorial. Computer , 29 (3), 31–44. https://doi.org/10.1109/2.485891
Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics , SMC -15 (4), 580–585. https://doi.org/10.1109/TSMC.1985.6313426
Kintner, J. A., Ammon, C. J., Cleveland, K. M., & Herman, M. (2018). Rupture processes of the 2013–2014 Minab earthquake sequence, Iran.Geophysical Journal International , 213 (3), 1898–1911. https://doi.org/10.1093/gji/ggy085
Kintner, J. A., Wauthier, C., & Ammon, C. J. (2019). InSAR and seismic analyses of the 2014–15 earthquake sequence near Bushkan, Iran: shallow faulting in the core of an anticline fold. Geophysical Journal International , 217 (2), 1011–1023. https://doi.org/10.1093/gji/ggz065
Kintner, J. A., Ammon, C. J., Homman, K., & Nyblade, A. (2020). Precise Relative Magnitude and Relative Location Estimates of Low-Yield Industrial Blasts in Pennsylvania. Bulletin of the Seismological Society of America , 110 (1), 226–240. https://doi.org/10.1785/012019163
Kintner, J. A., Cleveland, K. M., Ammon, C. J., & Nyblade, A. (2021). Local-Distance Seismic Event Relocation and Relative Magnitude Estimation, Applications to Mining Related Seismicity in the Powder River Basin, Wyoming. Bulletin of the Seismological Society of America , 111 (3), 1347–1364. https://doi.org/10.1785/0120200369
Krischer, L., Megies, T., Barsch, R., Beyreuther, M., Lecocq, T., Caudron, C., & Wassermann, J. (2015). ObsPy: a bridge for seismology into the scientific Python ecosystem. Computational Science & Discovery , 8 (1), 014003. https://doi.org/10.1088/1749-4699/8/1/014003
Kuang, W., Yuan, C., & Zhang, J. (2021). Real-time determination of earthquake focal mechanism via deep learning. Nature Communications , 12 (1), 1432. https://doi.org/10.1038/s41467-021-21670-x
Lay, T., Ye, L., Kanamori, H., & Satake, K. (2018). Constraining the Dip of Shallow, Shallowly Dipping Thrust Events Using Long‐Period Love Wave Radiation Patterns: Applications to the 25 October 2010 Mentawai, Indonesia, and 4 May 2018 Hawaii Island Earthquakes. Geophysical Research Letters , 45 (19), 10,342-10,349. https://doi.org/10.1029/2018GL080042
Li, Z., Meier, M. A., Hauksson, E., Zhan, Z., & Andrews, J. (2018). Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning. Geophysical Research Letters , 45 (10), 4773–4779. https://doi.org/10.1029/2018GL077870
McBrearty, I. W., Delorey, A. A., & Johnson, P. A. (2019). Pairwise Association of Seismic Arrivals with Convolutional Neural Networks.Seismological Research Letters , 90 (2A), 503–509. https://doi.org/10.1785/0220180326
Megies, T., Beyreuther, M., Barsch, R., Krischer, L., & Wassermann, J. (2011). ObsPy - what can it do for data centers and observatories?Annals of Geophysics . https://doi.org/10.4401/ag-4838
Meier, M. A., Ross, Z. E., Ramachandran, A., Balakrishna, A., Nair, S., Kundzicz, P., et al. (2019). Reliable Real-Time Seismic Signal/Noise Discrimination With Machine Learning. Journal of Geophysical Research: Solid Earth , 124 (1), 788–800. https://doi.org/10.1029/2018JB016661
Mousavi, S. M., & Beroza, G. C. (2020). A Machine‐Learning Approach for Earthquake Magnitude Estimation. Geophysical Research Letters ,47 (1), 1–7. https://doi.org/10.1029/2019GL085976
Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. (2020). Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Communications , 11 (1), 3952. https://doi.org/10.1038/s41467-020-17591-w
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine Learning in Python.Journal of Machine Learning Research , 12 , 2825–2830.
Perol, T., Gharbi, M., & Denolle, M. (2018). Convolutional neural network for earthquake detection and location. Science Advances ,4 (2), e1700578. https://doi.org/10.1126/sciadv.1700578
Ross, Z. E., Meier, M. A., Hauksson, E., & Heaton, T. H. (2018). Generalized seismic phase detection with deep learning. Bulletin of the Seismological Society of America , 108 (5), 2894–2901. https://doi.org/10.1785/0120180080
Ross, Z. E., Yue, Y., Meier, M., Hauksson, E., & Heaton, T. H. (2019). PhaseLink: A Deep Learning Approach to Seismic Phase Association.Journal of Geophysical Research: Solid Earth , 124 (1), 856–869. https://doi.org/10.1029/2018JB016674
Rouet-Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C. J., & Johnson, P. A. (2017). Machine Learning Predicts Laboratory Earthquakes. Geophysical Research Letters , 44 (18), 9276–9282. https://doi.org/10.1002/2017GL074677
Seydoux, L., Balestriero, R., Poli, P., Hoop, M. de, Campillo, M., & Baraniuk, R. (2020). Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. Nature Communications , 11 (1). https://doi.org/10.1038/s41467-020-17841-x
Suykens, J. A. K., & Vandewalle, J. (1999). Least Squares Support Vector Machine Classifiers. Applied and Computational Harmonic Analysis , 9 , 293–300. https://doi.org/10.1023/A:1018628609742
Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: A structure for efficient numerical computation. Computing in Science and Engineering , 13 (2), 22–30. https://doi.org/10.1109/MCSE.2011.37
Wessel, P., Luis, J. F., Uieda, L., Scharroo, R., Wobbe, F., Smith, W. H. F., & Tian, D. (2019). The Generic Mapping Tools Version 6.Geochemistry, Geophysics, Geosystems , 20 (11), 5556–5564. https://doi.org/10.1029/2019GC008515
Wessel, Paul, Smith, W. H. F., Scharroo, R., Luis, J., & Wobbe, F. (2013). Generic Mapping Tools: improved version released. Eos, Transactions American Geophysical Union , 94 (45), 409–410. https://doi.org/10.1002/2013EO450001
Yoon, C. E., O’Reilly, O., Bergen, K. J., & Beroza, G. C. (2015). Earthquake detection through computationally efficient similarity search. Science Advances , 1 (11), e1501057. https://doi.org/10.1126/sciadv.1501057
Zhang, X., Zhang, J., Yuan, C., Liu, S., Chen, Z., & Li, W. (2020). Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method. Scientific Reports ,10 (1), 1–12. https://doi.org/10.1038/s41598-020-58908-5
Zhang, Z., & Lin, Y. (2020). Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization. IEEE Transactions on Geoscience and Remote Sensing , 58 (10), 6900–6913. https://doi.org/10.1109/TGRS.2020.2977635
Zhu, L., Peng, Z., McClellan, J., Li, C., Yao, D., Li, Z., & Fang, L. (2019). Deep learning for seismic phase detection and picking in the aftershock zone of 2008 M7.9 Wenchuan Earthquake. Physics of the Earth and Planetary Interiors , 293 (May 2018), 106261. https://doi.org/10.1016/j.pepi.2019.05.004
Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. Geophysical Journal International , 216 (1), 261–273. https://doi.org/10.1093/gji/ggy423