Statistical analyses and characteristics of volcanic tremor on Stromboli Volcano (Italy)

1998 ◽  
Vol 60 (2) ◽  
pp. 75-88 ◽  
Author(s):  
S. Falsaperla ◽  
H. Langer ◽  
S. Spampinato
Keyword(s):  
Volcanic Tremor ◽  
Statistical Analyses ◽  
Stromboli Volcano

scholarly journals Spectral precursors of paroxysmal phases of Stromboli

1996 ◽  
Vol 39 (2) ◽  
Author(s):  
R. Carniel ◽  
F. Iacop
Keyword(s):  
Continuous Monitoring ◽  
Energy Content ◽  
Volcanic Tremor ◽  
Energy Concentration ◽  
Stromboli Volcano ◽  
Stable States ◽  
Spectral Content ◽  
Abrupt Changes ◽  
Intense Activity ◽  
Dangerous Activity

In this work we investigate the characteristics of the seismicity at Stromboli volcano during more than two years, i.e. from 11 May 1992 to 21 August 1994. The three paroxysmal phases of 1993 mark significant changes in the Strombolian activity; nevertheless, these are not the only ones observed. In fact, the energy content, both in terms of volcanic tremor and of number of events drops to very low values after the periods of intense activity, accompanied by a change in the spectral content of the tremor. However, equally abrupt changes in the frequency content, not accompanied by evident intensity variations, can be observed some weeks after the end of the crises. The volcano seems therefore to behave like a dynamical system with many «quite stable » states with abrupt transitions between them. An interesting observation is the appearance of an energy concentration in the spectral sectors below 3 Hz before more violent eruptive episodes; although the duration of such a phenomenon is variable, it has to be investigated as a possible precursor of potentially dangerous activity of the volcano. A continuous monitoring of the spectral content of volcanic tremor on Stromboli is confirmed to be an essential tool in order to understand the behaviour of Stromboli volcano and to try to forecast its paroxysmal phases.

scholarly journals Neural networks and dynamical system techniques for volcanic tremor analysis

1996 ◽  
Vol 39 (2) ◽  
Author(s):  
R. Carniel
Keyword(s):  
Dynamical System ◽  
State Space ◽  
Structural Changes ◽  
Volcanic Tremor ◽  
Lag Time ◽  
State Variables ◽  
Stromboli Volcano ◽  
Structural Configuration ◽  
Experimental Time Series ◽  
Tremor Analysis

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.

Evidence of VLP volcanic tremor in the band [0.2-0.5] Hz at Stromboli volcano, Italy

2005 ◽  
Vol 32 (17) ◽  
Author(s):  
E. De Lauro ◽  
S. De Martino ◽  
M. Falanga ◽  
M. Palo ◽  
R. Scarpa
Keyword(s):  
Volcanic Tremor ◽  
Stromboli Volcano

Structural changes of volcanic tremor at Stromboli volcano

2008 ◽  
Vol 171 (1-2) ◽  
pp. 103-117 ◽  
Author(s):  
Francesca Fattori Speranza ◽  
Roberto Carniel
Keyword(s):  
Structural Changes ◽  
Volcanic Tremor ◽  
Stromboli Volcano

scholarly journals The spatial amplitude distribution of volcanic tremor at Stromboli volcano (Italy)

1996 ◽  
Vol 39 (2) ◽  
Author(s):  
J. U. Mohnen ◽  
R. Schick
Keyword(s):  
Seismic Station ◽  
Vertical Component ◽  
Volcanic Tremor ◽  
Amplitude Distribution ◽  
Horizontal Component ◽  
Stromboli Volcano ◽  
Frequency Range ◽  
Spectral Radiation ◽  
First Approximation ◽  
Spectral Analyzer

A portable seismic station consisting of a three-component seismometer in conjunction with a spectral analyzer was deployed in May and June 1994 to record volcanic tremor in a wide area on Stromboli. For the reduction of path effects, tremor spectra were averaged over 164 observation points. They illustrate smooth and broadband spectral lobes in the frequency range between 1-12 Hz. It is suggestive that these overall spectra represent in a first approximation the spectral radiation caused by source processes. Identical and significant maxima at 2.65 Hz and 3.65 Hz are found in all components. No systematic distinction is found in the amplitude values and spectral forms for either horizontal component. The amplitude of the vertical component presents approximately one third that of the horizontal components. A subclassification of the data according to geological strata shows frequency dependent amplitude amplifications. Thick ash and lapilli beds reach a factor of four within frequencies from 4-6 Hz. The influence of these site effects seems minor below 2 Hz. The paper presents maps for each of the three components showing the distribution of the tremor amplitudes averaged over areas of 150 m by 150 m. Model curves derived from fluid-flow acoustics are compared with the tremor spectra.

Structuring Situational Information

2015 ◽  
Vol 20 (3) ◽  
pp. 176-189 ◽  
Author(s):  
John F. Rauthmann
Keyword(s):  
Statistical Analyses ◽  
Current Work ◽  
Common Language ◽  
Road Map ◽  
Taxonomic Approach ◽  
Situational Information

Abstract. There is as yet no consensually agreed-upon situational taxonomy. The current work addresses this issue and reviews extant taxonomic approaches by highlighting a “road map” of six research stations that lead to the observed diversity in taxonomies: (1) theoretical and conceptual guidelines, (2) the “type” of situational information studied, (3) the general taxonomic approach taken, (4) the generation of situation pools, (5) the assessment and rating of situational information, and (6) the statistical analyses of situation data. Current situational taxonomies are difficult to integrate because they follow different paths along these six stations. Some suggestions are given on how to spur integrated taxonomies toward a unified psychology of situations that speaks a common language.

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