Unsupervised Feature Selection for Pattern Discovery in Seismic Wavefields
Andreas Köhler, Matthias Ohrnberger, Carsten Riggelsen, Frank Scherbaum;
JMLR W&P 4:106-121, 2008.
This study presents an unsupervised feature selection approach for the discovery
of significant patterns in seismic wavefields. We iteratively reduce the number
of features generated from seismic time series by first considering significance
of individual features. Significance testing is done by assessing the randomness
of the time series with the Wald-Wolfowitz runs test and by comparing observed and
theoretical variability of features. In a second step the in-between feature
dependencies are assessed based on correlation hunting in feature subsets using
Self-Organizing Maps (SOMs). We show the improved discriminative power of our
procedure compared to manually selected feature subsets by cross-validation applied
to synthetic seismic wavefield data. Furthermore, we apply the method to real-world
data with the aim to define suitable features for earthquake detection and seismic
phase classification in seismic recordings.