Premium
Application of Support Vector Machine to the classification of volcanic tremor at Etna, Italy
Author(s) -
Masotti M.,
Falsaperla S.,
Langer H.,
Spampinato S.,
Campanini R.
Publication year - 2006
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2006gl027441
Subject(s) - support vector machine , lava , volcano , geology , artificial intelligence , classifier (uml) , pattern recognition (psychology) , random forest , seismology , impact crater , computer science , physics , astronomy
We applied an automatic pattern recognition technique, known as Support Vector Machine (SVM), to classify volcanic tremor data recorded during different states of activity at Etna volcano, Italy. The seismic signal was recorded at a station deployed 6 km southeast of the summit craters from 1 July to 15 August, 2001, a time span encompassing episodes of lava fountains and a 23 day‐long effusive activity. Trained by a supervised learning algorithm, the classifier learned to recognize patterns belonging to four classes, i.e., pre‐eruptive, lava fountains, eruptive, and post‐eruptive. Training and test of the classifier were carried out using 425 spectrogram‐based feature vectors. Following cross‐validation with a random subsampling strategy, SVM correctly classified 94.7 ± 2.4% of the data. The performance was confirmed by a leave‐one‐out strategy, with 401 matches out of 425 patterns. Misclassifications highlighted intrinsic fuzziness of class memberships of the signals, particularly during transitional phases.