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.
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