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Semiautomated tremor detection using a combined cross-correlation and neural network approach

Journal of Geophysical Research B: Solid Earth

By:
, ,
DOI: 10.1002/jgrb.50345

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Abstract

Despite observations of tectonic tremor in many locations around the globe, the emergent phase arrivals, low‒amplitude waveforms, and variable event durations make automatic detection a nontrivial task. In this study, we employ a new method to identify tremor in large data sets using a semiautomated technique. The method first reduces the data volume with an envelope cross‒correlation technique, followed by a Self‒Organizing Map (SOM) algorithm to identify and classify event types. The method detects tremor in an automated fashion after calibrating for a specific data set, hence we refer to it as being “semiautomated”. We apply the semiautomated detection algorithm to a newly acquired data set of waveforms from a temporary deployment of 13 seismometers near Cholame, California, from May 2010 to July 2011. We manually identify tremor events in a 3 week long test data set and compare to the SOM output and find a detection accuracy of 79.5%. Detection accuracy improves with increasing signal‒to‒noise ratios and number of available stations. We find detection completeness of 96% for tremor events with signal‒to‒noise ratios above 3 and optimal results when data from at least 10 stations are available. We compare the SOM algorithm to the envelope correlation method of Wech and Creager and find the SOM performs significantly better, at least for the data set examined here. Using the SOM algorithm, we detect 2606 tremor events with a cumulative signal duration of nearly 55 h during the 13 month deployment. Overall, the SOM algorithm is shown to be a flexible new method that utilizes characteristics of the waveforms to identify tremor from noise or other seismic signals.

Geospatial Extents

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Semiautomated tremor detection using a combined cross-correlation and neural network approach
Series title:
Journal of Geophysical Research B: Solid Earth
DOI:
10.1002/jgrb.50345
Volume
118
Issue:
9
Year Published:
2013
Language:
English
Publisher:
American Geophysical Union
Contributing office(s):
Earthquake Science Center
Description:
20 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Journal of Geophysical Research B: Solid Earth
First page:
4827
Last page:
4846
Number of Pages:
20
Country:
United States
State:
California
Other Geospatial:
Cholame