Difference between revisions of "Machine Learning"
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== Information about seismic networks == | == Information about seismic networks == | ||
| + | Summary of IRIS Seismic Data Management especially EarthScope | ||
| + | *[http://hypocenter.usc.edu/research/BigData/IRIS_Earthscope.pdf Paper] | ||
| + | *[http://hypocenter.usc.edu/research/BigData/NorthAmericanSeismicNetworks.pdf Paper] | ||
== Information about dense Seismic Arrays == | == Information about dense Seismic Arrays == | ||
Revision as of 20:41, 11 May 2018
Machine learning technologies are progressing rapidly and we want to apply these new technologies to the earthquake system science problems we are working on.
Contents
Information about seismic networks
Summary of IRIS Seismic Data Management especially EarthScope
Information about dense Seismic Arrays
The deployment of dense seismic arrays has produced compelling new views. These arrays tend to be short lived, but provide fascinating new views of seismic processes. These arrays are going to become much more commonplace. As one example, Pacific Gas and Electric is going to install seismic sensors on new electrical meters in Northern California.
Example of Deep Learning in Seismology
Two recent papers from SCEC collaborators using deep learning and seismic waveform data.
Ross, Z. E., Meier, M.-A., Hauksson, E., and T. H. Heaton (2018). Generalized seismic phase detection with deep learning, Bull. Seismol. Soc. Am.,
Ross, Z. E., Meier, M.-A., and E. Hauksson (2018). P-wave arrival picking and first-motion polarity determination with deep learning, J. Geophys. Res.-Solid Earth,
Related Research Papers
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XPrize approach
Below is an example of and Xprize type activity for Earthquake Forecasting: The QuakeFinder group tries to use electromagnetic signals to detect earthquake precursors. As I understand it, the prize was not awarded, but we might learn from this attempt.
IRIS Data Access Web Services
Scott Callaghan
Scott presented a staff tutorial on machine learning
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