Automatic Discrimination among Landslide, Explosion-Quake, and Microtremor Seismic Signals at Stromboli Volcano using Neural Networks

In this article we report on the implementation of an automatic system
for discriminating landslide seismic signals on Stromboli island (southern Italy). This
is a critical point for monitoring the evolution of this volcanic island, where at the
end of 2002 a violent tsunami occurred, triggered by a big landslide. We have devised
a supervised neural system to discriminate among landslide, explosion-quake, and
volcanic microtremor signals. We first preprocess the data using a compact representation
of the seismic records. Both spectral features and amplitude-versus-time
information have been extracted from the data to characterize the different types of
events. As a second step, we have set up a supervised classification system, trained
using a subset of data (the training set) and tested on another data set (the test set)
not used during the training stage. The automatic system that we have realized is
able to correctly classify 99% of the events in the test set for both explosion-quake/
landslide and explosion-quake/microtremor couples of classes, 96% for landslide/
microtremor discrimination, and 97% for three-class discrimination (landslides/
explosion-quakes/microtremor). Finally, to determine the intrinsic structure of the
data and to test the efficiency of our parameterization strategy, we have analyzed the
preprocessed data using an unsupervised neural method. We apply this method to
the entire dataset composed of landslide, microtremor, and explosion-quake signals.
The unsupervised method is able to distinguish three clusters corresponding to the
three classes of signals classified by the analysts, demonstrating that the parameterization
technique characterizes the different classes of data appropriately.

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