1,195 research outputs found
Integrated inversion of ground deformation and magnetic data at Etna volcano using a genetic algorithm technique
Geodetic and magnetic investigations have been playing an increasingly important role in studies on Mt. Etna
eruptive processes. During ascent, magma interacts with surrounding rocks and fluids, and inevitably crustal deformation
and disturbances in the local magnetic field are produced. These effects are generally interpreted separately
from each other and consistency of interpretations obtained from different methods is qualitatively
checked only a posteriori. In order to make the estimation of source parameters more robust we propose an integrated
inversion from deformation and magnetic data that leads to the best possible understanding of the underlying
geophysical process. The inversion problem was formulated following a global optimization approach based
on the use of genetic algorithms. The proposed modeling inversion technique was applied on field data sets
recorded during the onset of the 2002-2003 Etna flank eruption. The deformation pattern and the magnetic anomalies
were consistent with a piezomagnetic effect caused by a dyke intrusion propagating along the NE direction
fem and ann combined approach for predicting pressure source parameters at etna volcano
Abstract. A hybrid approach for forward and inverse geophysical modeling, based on Artificial Neural Networks (ANN) and Finite Element Method (FEM), is proposed in order to properly identify the parameters of volcanic pressure sources from geophysical observations at ground surface. The neural network is trained and tested with a set of patterns obtained by the solutions of numerical models based on FEM. The geophysical changes caused by magmatic pressure sources were computed developing a 3-D FEM model with the aim to include the effects of topography and medium heterogeneities at Etna volcano. ANNs are used to interpolate the complex non linear relation between geophysical observations and source parameters both for forward and inverse modeling. The results show that the combination of neural networks and FEM is a powerful tool for a straightforward and accurate estimation of source parameters in volcanic regions
The HOTSAT volcano monitoring system based on combined use of SEVIRI and MODIS multispectral data
Spaceborne remote sensing of high-temperature volcanic features offers an
excellent opportunity to monitor the onset and development of new eruptive activity. To provide a basis for real-time response during eruptive events, we designed and developed the volcano monitoring system that we call HOTSAT. This multiplatform system can elaborate both Moderate Resolution Imaging Spectroradiometer (MODIS) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, and it is here applied to the monitoring of the Etna volcano. The main advantage of this approach is that the different features of both of these sensors can be used. It can be refreshed every 15 min due to the high frequency of the SEVIRI acquisition, and it can detect smaller and/or less intense thermal anomalies through the MODIS data. The system consists of data preprocessing, detection of volcano hotspots, and radiative power estimation. To locate thermal anomalies, a new contextual algorithm is introduced that takes advantage of both the spectral and spatial comparison methods. The derivation of the radiative power is carried out at all ‘hot’ pixels using the middle infrared radiance technique. The whole processing chain was tested during the 2008 Etna eruption. The results show the robustness of the system after it detected the lava fountain that occurred on May 10 through the SEVIRI data, and the very beginning of the eruption on May 13 through the MODIS data analysis
Active veterinary and entomological surveillance to assess emerging vector-borne disease risk in the Autonomous Province of Bolzano (Italy)
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FEM and ANN combined approach for predicting pressure source
A hybrid approach for forward and inverse geophysical
modeling, based on Artificial Neural Networks
(ANN) and Finite Element Method (FEM), is proposed in
order to properly identify the parameters of volcanic pressure
sources from geophysical observations at ground surface.
The neural network is trained and tested with a set of
patterns obtained by the solutions of numerical models based
on FEM. The geophysical changes caused by magmatic pressure
sources were computed developing a 3-D FEM model
with the aim to include the effects of topography and medium
heterogeneities at Etna volcano. ANNs are used to interpolate
the complex non linear relation between geophysical observations
and source parameters both for forward and inverse
modeling. The results show that the combination of
neural networks and FEM is a powerful tool for a straightforward
and accurate estimation of source parameters in volcanic
regions
A multi-agent system with distributed bayesian reasoning for network fault diagnosis
In this paper, an innovative approach to perform distributed Bayesian inference using a multi-agent architecture is presented. The final goal is dealing with uncertainty in network diagnosis, but the solution can be of applied in other fields. The validation testbed has been a P2P streaming video service. An assessment of the work is presented, in order to show its advantages when it is compared with traditional manual processes and other previous systems
Non-linear analysis of geomagnetic time series from Etna volcano
International audienceAn intensive nonlinear analysis of geomagnetic time series from the magnetic network on Etna volcano was carried out to investigate the dynamical behavior of magnetic anomalies in volcanic areas. The short-term predictability of the geomagnetic time series was evaluated to establish a possible low-dimensional deterministic dynamics. We estimated the predictive ability of both a nonlinear forecasting technique and a global autoregressive model by comparing the prediction errors. Our findings highlight that volcanomagnetic signals are the result of complex processes that cannot easily be predicted. There is slight evidence based on nonlinear predictions, that the geomagnetic time series are to be governed by many variables, whose time evolution could be better regarded as arising from complex high dimensional processes
Non-linear analysis of geomagnetic time series from Etna volcano
An intensive nonlinear analysis of geomagnetic
time series from the magnetic network on Etna volcano was
carried out to investigate the dynamical behavior of magnetic
anomalies in volcanic areas. The short-term predictability of
the geomagnetic time series was evaluated to establish a possible
low-dimensional deterministic dynamics. We estimated
the predictive ability of both a nonlinear forecasting technique
and a global autoregressive model by comparing the
prediction errors. Our findings highlight that volcanomagnetic
signals are the result of complex processes that cannot
easily be predicted. There is slight evidence based on
nonlinear predictions, that the geomagnetic time series are to
be governed by many variables, whose time evolution could
be better regarded as arising from complex high dimensional
processes
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