A volcano can be considered as a dynamical system, and each time series recorded at a volcano can be interpreted as one of its observables. It is therefore theoretically possible to extract, even from a single time series, information about the underlying governing system. This is done through a procedure called "embedding" that is based on the intuitive statement that the only time series available carries with it information also about the time evolution of other parameters that we are not able to sample or observe. Carrying out this embedding procedure requires estimates of key parameters such as the optimal delay time and a proper embedding dimension. Other independent but often conceptually similar procedures allow decompositions of the time series into components that may in turn be associated to different source processes. The key to the characterization of volcanic regimes is a process of data reduction, aimed at parsing the amount of data into its most useful components which can then facilitate the interpretation of the system. The approaches presented here can be used to conduct such a data reduction phase, and the reduced data stream can be used not only for characterizing different volcanic regimes but also for determining transitions between them, examining their relationship with external or internal events such as tectonic or volcano-tectonic seismic events, looking for precursors of paroxysmal eruptive phases etc. These results can become additional inputs for physical models in order to understand in detail the physical changes that occurred in the volcanic system and their possible consequences. In this paper, the existing literature on this subject will be reviewed and the prospects of future research will be discussed.

Characterization of volcanic regimes and identification of significant transitions using geophysical data: A review

CARNIEL, Roberto
2014-01-01

Abstract

A volcano can be considered as a dynamical system, and each time series recorded at a volcano can be interpreted as one of its observables. It is therefore theoretically possible to extract, even from a single time series, information about the underlying governing system. This is done through a procedure called "embedding" that is based on the intuitive statement that the only time series available carries with it information also about the time evolution of other parameters that we are not able to sample or observe. Carrying out this embedding procedure requires estimates of key parameters such as the optimal delay time and a proper embedding dimension. Other independent but often conceptually similar procedures allow decompositions of the time series into components that may in turn be associated to different source processes. The key to the characterization of volcanic regimes is a process of data reduction, aimed at parsing the amount of data into its most useful components which can then facilitate the interpretation of the system. The approaches presented here can be used to conduct such a data reduction phase, and the reduced data stream can be used not only for characterizing different volcanic regimes but also for determining transitions between them, examining their relationship with external or internal events such as tectonic or volcano-tectonic seismic events, looking for precursors of paroxysmal eruptive phases etc. These results can become additional inputs for physical models in order to understand in detail the physical changes that occurred in the volcanic system and their possible consequences. In this paper, the existing literature on this subject will be reviewed and the prospects of future research will be discussed.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1084467
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 34
  • ???jsp.display-item.citation.isi??? 31
social impact