A new method using a machine learning technique is applied to event
classification and detection at seismic networks. This method is applicable to a
variety of network sizes and settings. The algorithm makes use of a small catalogue
of known observations across the entire network. Two attributes, the polarization
and frequency content, are used as input to regression. These attributes are
extracted at predicted arrival times for P and S waves using only an approximate
velocity model, as attributes are calculated over large time spans. This method of
waveform characterization is shown to be able to distinguish between blasts and
earthquakes with 99 percent accuracy using a network of 13 stations located in
Southern California. The combination of machine learning with generalized
waveform features is further applied to event detection in Oklahoma, United States.
The event detection algorithm makes use of a pair of unique seismic phases to locate
events, with a precision directly related to the sampling rate of the generalized
waveform features. Over a week of data from 30 stations in Oklahoma, United States
are used to automatically detect 25 times more events than the catalogue of the local
geological survey, with a false detection rate of less than 2 per cent. This method
provides a highly confident way of detecting and locating events. Furthermore, a
large number of seismic events can be automatically detected with low false alarm,
allowing for a larger automatic event catalogue with a high degree of trust.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/36867 |
| Date | January 2017 |
| Creators | Reynen, Andrew |
| Contributors | Audet, Pascal |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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