TY - GEN
T1 - On the use of variational mode decomposition for seismic event detection
AU - Proano, Esteban
AU - Benitez, Diego S.
AU - Lara-Cueva, Roman
AU - Ruiz, Mario
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In this paper, we investigate the use of Variational Mode Decomposition (VMD) for the analysis of seismic signals obtained from the Cotopaxi Volcano in Ecuador. The VMD method is proposed here as a method for noise attenuation to improve the event detection and the identification of the starting and end points of seismic events. The main advantage of the VMD method over previous studied methods is its robustness for reducing noise and the number of features necessary to distinguish amongst the signals. Preliminary analysis shows that seismic events such as volcano-tectonic (VT) earthquakes, and long-period (LP) events, can be identified after applying the VMD to the seismic signal due to the fact that the modes obtained are considerably different between these types of seismic events, therefore this decomposition could also be used to extract features for an automatic classifier. Further observations show that the same process used for obtaining the modes of the signal can also be applied to detect the presence of events using a fixed-size window and an amplitude threshold, the numerical results show a 99.26% accuracy for obtaining the events onset and ending points.
AB - In this paper, we investigate the use of Variational Mode Decomposition (VMD) for the analysis of seismic signals obtained from the Cotopaxi Volcano in Ecuador. The VMD method is proposed here as a method for noise attenuation to improve the event detection and the identification of the starting and end points of seismic events. The main advantage of the VMD method over previous studied methods is its robustness for reducing noise and the number of features necessary to distinguish amongst the signals. Preliminary analysis shows that seismic events such as volcano-tectonic (VT) earthquakes, and long-period (LP) events, can be identified after applying the VMD to the seismic signal due to the fact that the modes obtained are considerably different between these types of seismic events, therefore this decomposition could also be used to extract features for an automatic classifier. Further observations show that the same process used for obtaining the modes of the signal can also be applied to detect the presence of events using a fixed-size window and an amplitude threshold, the numerical results show a 99.26% accuracy for obtaining the events onset and ending points.
UR - http://www.scopus.com/inward/record.url?scp=85063906231&partnerID=8YFLogxK
U2 - 10.1109/ROPEC.2018.8661428
DO - 10.1109/ROPEC.2018.8661428
M3 - Contribución a la conferencia
AN - SCOPUS:85063906231
T3 - 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
BT - 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018
Y2 - 14 November 2018 through 16 November 2018
ER -