The time-correlation properties in the hourly time variability of volcano-magnetic data measured at the active volcano Mt. Etna, Sicily (southern Italy), are investigated by using the detrended fluctuation analysis (DFA). DFA is a data processing method that allows for the detection of scaling behaviors in observational time series even in the presence of nonstationarities. The procedure adopted has revealed unambiguous link between the dynamics of the measured data and the recent eruptive episode of the volcano occurred on October 27, 2002.Published1921-1929partially_ope
A rigorous analysis of the timing and location of flank eruptions of Mount Etna on Sicily is importa...
In the first part of this work, we make use of two non-parametric statistical pattern recognition al...
Mt Etna is a well monitored basaltic volcano for which high-quality, multidisciplinary data set are ...
The time-correlation properties in the hourly time variability of volcano-magnetic data measured at ...
International audienceWe applied the Multifractal Detrended Fluctuation Analysis (MF-DFA), which all...
Volcanomagnetic anomalies have been mostly observed during strong eruptions. Our aim is to improve t...
Understanding the underlying structure of data from volcano monitoring is essential to identify prec...
International audienceAn intensive nonlinear analysis of geomagnetic time series from the magnetic n...
The evolution of scaling characteristics of the local geomagnetic field and of the seismicity at Etn...
The aim of this work is to improve our understanding of the long-period (LP) source mechanism at Mou...
Abstract—An analysis in terms of time correlation functions has been applied to the time distributio...
Seismic data from the MVT-SLN sesmic station located 7 km from the summit area of Mt Etna volcano, w...
In the first part of this work, we make use of two non-parametric statistical pattern recognition al...
A new statistical nonparametric pattern recognition algorithm is used to identify the phenomenology ...
Seismicity time properties of the Etna Volcano (Italy) are investigated through a systematic pattern...
A rigorous analysis of the timing and location of flank eruptions of Mount Etna on Sicily is importa...
In the first part of this work, we make use of two non-parametric statistical pattern recognition al...
Mt Etna is a well monitored basaltic volcano for which high-quality, multidisciplinary data set are ...
The time-correlation properties in the hourly time variability of volcano-magnetic data measured at ...
International audienceWe applied the Multifractal Detrended Fluctuation Analysis (MF-DFA), which all...
Volcanomagnetic anomalies have been mostly observed during strong eruptions. Our aim is to improve t...
Understanding the underlying structure of data from volcano monitoring is essential to identify prec...
International audienceAn intensive nonlinear analysis of geomagnetic time series from the magnetic n...
The evolution of scaling characteristics of the local geomagnetic field and of the seismicity at Etn...
The aim of this work is to improve our understanding of the long-period (LP) source mechanism at Mou...
Abstract—An analysis in terms of time correlation functions has been applied to the time distributio...
Seismic data from the MVT-SLN sesmic station located 7 km from the summit area of Mt Etna volcano, w...
In the first part of this work, we make use of two non-parametric statistical pattern recognition al...
A new statistical nonparametric pattern recognition algorithm is used to identify the phenomenology ...
Seismicity time properties of the Etna Volcano (Italy) are investigated through a systematic pattern...
A rigorous analysis of the timing and location of flank eruptions of Mount Etna on Sicily is importa...
In the first part of this work, we make use of two non-parametric statistical pattern recognition al...
Mt Etna is a well monitored basaltic volcano for which high-quality, multidisciplinary data set are ...