Open AccessNeural networks and dynamical system techniques for volcanic tremor analysis
Author(s) -
Roberto Carniel
Publication year - 1996
Publication title -
annals of geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 60
eISSN - 2037-416X
pISSN - 1593-5213
DOI - 10.4401/ag-3967
Subject(s) - volcano , dimension (graph theory) , manifold (fluid mechanics) , embedding , phase space , dynamical systems theory , geology , dynamical system (definition) , state space , artificial neural network , state (computer science) , center manifold , physics , seismology , computer science , statistical physics , mathematics , bifurcation , artificial intelligence , algorithm , pure mathematics , engineering , nonlinear system , mechanical engineering , statistics , quantum mechanics , thermodynamics , hopf bifurcation
A volcano can be seen as a dynamical system, the number of state variables being its dimension N. The state is usually confined on a manifold with a lower dimension f, manifold which is characteristic of a persistent «structural configuration». A change in this manifold may be a hint that something is happening to the dynamics of the volcano, possibly leading to a paroxysmal phase. In this work the original state space of the volcano dynamical system is substituted by a pseudo state space reconstructed by the method of time-delayed coordinates, with suitably chosen lag time and embedding dimension, from experimental time series of seismic activity, i.e. volcanic tremor recorded at Stromboli volcano. The monitoring is done by a neural network which first learns the dynamics of the persistent tremor and then tries to detect structural changes in its behaviour