Detecting imminent eruptive activity at Mt Etna, Italy, in 2007–2008 through pattern classification of volcanic tremor data

2011 ◽  
Vol 200 (1-2) ◽  
pp. 1-17 ◽  
Author(s):  
H. Langer ◽  
S. Falsaperla ◽  
A. Messina ◽  
S. Spampinato ◽  
B. Behncke
Keyword(s):  
Pattern Classification ◽  
Volcanic Tremor ◽  
Eruptive Activity ◽  
Mt Etna

“Failed” eruptions revealed by pattern classification analysis of gas emission and volcanic tremor data at Mt. Etna, Italy

2013 ◽  
Vol 103 (1) ◽  
pp. 297-313 ◽  
Author(s):  
S. Falsaperla ◽  
B. Behncke ◽  
H. Langer ◽  
M. Neri ◽  
G. G. Salerno ◽  
...  
Keyword(s):  
Pattern Classification ◽  
Volcanic Tremor ◽  
Gas Emission ◽  
Classification Analysis ◽  
Mt Etna

scholarly journals Synopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data at Mt Etna, Italy

2009 ◽  
Vol 178 (2) ◽  
pp. 1132-1144 ◽  
Author(s):  
H. Langer ◽  
S. Falsaperla ◽  
M. Masotti ◽  
R. Campanini ◽  
S. Spampinato ◽  
...  
Keyword(s):  
Pattern Classification ◽  
Volcanic Tremor ◽  
Classification Techniques ◽  
Mt Etna

scholarly journals Clustering activity at Mt Etna based on volcanic tremor: A case study

2021 ◽  
Author(s):  
Giuseppe Nunnari
Keyword(s):  
Machine Learning ◽  
Volcanic Activity ◽  
Roc Curve ◽  
Volcanic Tremor ◽  
Machine Learning Algorithms ◽  
Etna Volcano ◽  
Mt Etna ◽  
Geophysical Signal

AbstractThis paper deals with the classification of volcanic activity into three classes, referred to as Quite, Strombolian and Paroxysm. The main purpose is to give a measure of the reliability with which such a classification, typically carried out by experts, can be performed by Machine Learning algorithms, by using the volcanic tremor as a feature. Both supervised and unsupervised methods are considered. It is experimentally shown that at least the Paroxysm activity can be reliably classified. Performances are rigorously assessed, in comparison with the classification made by expert volcanologists, in terms of popular indices such as the f1-score and the Area under the ROC curve (AuC). The work is basically a case study carried out on a dataset recorded in the area of the Mt Etna volcano. However, as volcanic tremor is a geophysical signal widely available, considered methods and strategies can be easily applied to similar volcanic areas.

scholarly journals Features of seismic events and volcanic tremor during the preliminary stages of the 1991-1993 eruption of Mt. Etna

1996 ◽  
Vol 39 (2) ◽  
Author(s):  
G. Lombardo ◽  
G Coco ◽  
M. Corrao ◽  
S. Gresta
Keyword(s):  
Low Frequency ◽  
Volcanic Tremor ◽  
Temporal Distribution ◽  
Spectral Signature ◽  
Seismic Events ◽  
Spectral Features ◽  
Eruptive Activity ◽  
Eruptive Event ◽  
Mt Etna ◽  
Dominant Frequencies

The study of the spectral features of volcanic tremor and low frequency events (l.f.e.) recorded before and during the preliminary phases of the powerful 1991-1993 eruption of Mt. Etna is briefly described. Significant modifications were observed in the spectral signature of l.f.e. before the onset of the eruptive event, as well as in the temporal distribution of the volcanic tremor dominant frequencies. We interpret both l.f.e. and tremor changes in terms of a spatial modification of the source, as the paroxysmal eruptive activity is approaching. Such findings also appear quite interesting for the identification of markers of the modifications which some seismic events of the volcano undergo in the early stages heading the occurrence of an eruption.

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