Publication Date:
2017-04-04
Description:
Landslides along the Sciara del Fuoco flank of Stromboli volcano are generally accompanied
by c1istinctive seismic signals which can be used for srudying this phenomenon.
These signals are characterìzed by a spectral content with higher frequencies
and a wider band than the typical explosion quakes and volcanic tremor signals which
are continuously recorded at Stromboli. Furthermore their amplirude envelope usually
shows a cigar-like shape. These two fearures make the detection of such signals quite
easy. The detection of landslides at Stromboli has shown to be an important shortterm
precursor of effusive eruptions. Before the Feb. 27th 2007 eruption, the opening
of the effusive vents was preceded by few hours oI increased occurrence of landslide
signals (Martini et al., 2007). Furthermore since the Sciara del Fuoco has shown significant
instabilities during the 2002-2003 eruption, the automatic detection of landslide
signals is an important monitoring tool for notifying variations in the stability of this
flank. We propose a technique based on a Multi Layer Perceptron (MLP) neural network
which has shown excellent performances. The network is composed of two layer
of neurons, the hidden and the output. The hidden layer is composed of 4 neurons
while the output layer is composed by a single neuron whose output value ranges
between Oand 1, with values higher than a given threshold (e.g. 0.5) meaning positive
detection. The continuous seismic signals are analysed using moving windows of 24 s,
with an overlap of 12 s. For each of these windows the neural output is computed.
The waveforms of each time window are parametrized using both their spectrogram
and their amplirude envelope. The spectrogram is described using the Linear Preclictive
Cocling (L'PC) technique which allows to represent the spectral content using a limited
number of coefficients. The whole signal is c1ivided into 8 sub-windows of 5.12 s
length, with an overlapping of 2.56 s. For each sub-window we compute 6 LPC coefficients,
so each spectrogram is described by only 48 coefficients. The amplirude envelope
is defined by computing the c1ifference between the maximum and minimum value
over 1 s sub-windows obtaining 24 coefficients. In conclusion we use an input vector
composed of 72 elements (48+24). This vector has shown to be an efficient and
compact representation of the raw signal (composed of 1200 samples) (Esposito et al.
2006). The dataset used for determining the network parameters is composed of 537
signals, c1ivided in two classes: 267 landslide signals and 270 other signals (explosions
and tremor). The classification of these signals has been performed by analysts. The training is carried out using subsets of 5/8 of the total dataset. The testing subsets are
composed by the remaining 3/8. The network has shown a performance of about
98.7%. This value is an average over 6 random permutations of the dataset. A preliminary
real-rime automatic system has already been implemented. This system performs
continuous analysis of the seismic signals, publishing them on internal web pages.
It allows a detection of the landslides and a comparison with the past activity on
arbitrary rime intervals.
Description:
Published
Description:
Nicolosi (Catania)
Description:
1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
Description:
3.1. Fisica dei terremoti
Description:
3.6. Fisica del vulcanismo
Description:
open
Keywords:
Automatic detection
;
landslides
;
Stromboli
;
neural network analysis
;
seismic signals
;
04. Solid Earth::04.02. Exploration geophysics::04.02.99. General or miscellaneous
;
04. Solid Earth::04.02. Exploration geophysics::04.02.06. Seismic methods
;
04. Solid Earth::04.02. Exploration geophysics::04.02.07. Instruments and techniques
;
04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology
;
04. Solid Earth::04.06. Seismology::04.06.10. Instruments and techniques
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
Conference paper
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