Andò, B.

Environmental parameters can seriously affect the performances of continuously running spring gravimeters. Temperature is a primary interfering quantity and its effect must be reduced through algorithms implementing a suitable compensation scheme. Algorithms to reduce the signals coming from continuously running gravimeters for the effect of meteorological perturbations have been developed and implemented in tools running in offline mode. The need for “on the fly” processing emerges when the recorded signals are used for volcano monitoring purposes, since any information on the volcanic phenomena under development must be assessed immediately. In this paper the implementation, in a dedicated LabVIEW application, of an algorithm performing temperature reduction on gravity signals is discussed and features of the software’s user interface are presented.

Gravity measurements are utilized at active volcanoes to detect mass changes linked to magma transfer processes and thus to recognize forerunners to paroxysmal volcanic events. Continuous gravity measurements are now increasingly performed at sites very close to active craters, where there is the greatest chance to detect meaningful gravity changes. Unfortunately, especially when used against the adverse environmental conditions usually encountered at such places, gravimeters have been proved to be affected by meteorological parameters, mainly by changes in the atmospheric temperature. The pseudo-signal generated by these perturbations is often stronger than the signal generated by actual changes in the gravity field. Thus, the implementation of well-performing algorithms for reducing the gravity signal for the effect of meteorological parameters is vital to obtain sequences useful from the volcano surveillance standpoint. In the present paper, a Neuro-Fuzzy algorithm, which was already proved to accomplish the required task satisfactorily, is tested over a data set from three gravimeters which worked continuously for about 50 days at a site far away from active zones, where changes due to actual fluctuation of the gravity field are expected to be within a few microgal. After accomplishing the reduction of the gravity series, residuals are within about 15μGal peak-to-peak, thus confirming the capabilities of the Neuro-Fuzzy algorithm under test of performing the required task satisfactorily.

The laboratory experiments can help the undergraduate students to gain a better understanding the theoretical problem. This paper proposes an educational tool made up a user-friendly interface controlling experimental boards and instrumentation device with a new approach based on FieldPointTM module and the web publishing tool of the LabVIEW. The proposed system allows students to improve their knowledge in the field of optical sensing devices, virtual instrumentation, data acquisition systems, and signal processing.

Video surveillance systems are consolidated techniques for the monitoring of eruptive phenomena in volcanic areas. Along with this systems, which use standard video cameras, sometimes people working on this field make use of infrared cameras providing useful information about the thermal evolution of the eruptions. The surveillance of the Mount Etna volcano in Sicily, Italy, is in charge of the Istituto Nazionale di Geofisica e di Vulcanologia - Catania site, and a large amount of monitoring systems are installed along the mountain. Data transmission between these devices and the surveillance sites is a serious task, especially when a large transmission band is required. Moreover, in the case of image storing large memorization capabilities are mandatory. In this paper a new methodology is presented, which aims to improve the performances of surveillance systems in terms of transmission band and storing feature; the proposed methodology is based on the real-time thermo graphic analysis of the monitored area which can provide information on the on going activity and adapt the image transmission rate as well.

The experience of several authors has shown that continuous measurements of the gravity field, accomplished through spring devices, are strongly affected by changes of the ambient temperature. The apparent, temperature-driven, gravity changes can be up to one order of magnitude higher than the expected changes of the gravity field. Since these effects are frequency-dependent and instrument-related, they must be removed through non-linear techniques and in a case-by-case fashion. Past studies have demonstrated the effectiveness of a Neuro-Fuzzy algorithm as a tool to reduce continuous gravity sequences for the effect of external temperature changes. In the present work, an upgraded version of this previously employed algorithm is tested against the signal from a gravimeter, which was installed in two different sites over consecutive 96-day and 163-day periods. The better performance of the new algorithm with respect to the previous one is proven. Besides, inferences about the site and/or seasonal dependence of the model structure are reported.

Video surveillance systems are consolidated techniques for monitoring eruptive phenomena in volcanic areas. Along with these systems, which use standard video cameras, people working in this field sometimes make use of infrared cameras providing useful information about the thermal evolution of eruptions. Real-time analysis of the acquired frames is required, along with image storing, to analyze and classify the activity of volcanoes. Human effort and large storing capabilities are hence required to perform monitoring tasks. In this paper we present a new strategy aimed at improving the performance of video surveillance systems in terms of human-independent image processing and storing optimization. The proposed methodology is based on real-time thermo-graphic analysis of the area considered. The analysis is performed by processing images acquired with an IR camera and extracting information about meaningful volcanic events. Two software tools were developed. The first provides information about the activity being monitored and automatically adapts the image storing rate. The second tool automatically produces useful information about the eruptive activity encompassed by a selected frame sequence. The software developed includes a suitable user interface allowing for convenient management of the acquired images and easy access to information about the volcanic activity monitored.

In this paper, a novel methodology to measure trajectory and terminal velocity of volcanic ash in laboratory is presented. The methodology consists of: i) planning a lab-scale experiment in order to reproduce the sedimentation processes of fine volcanic ash based on the principle of dynamic similarity; ii) realizing the experimental set-up using a glass tank filled with glycerine, a web-cam based vision system and a dedicated image post processing tool able to estimate the position and the terminal velocity of any particle falling in the tank; iii) performing a calibration procedure to accurately estimate the uncertainty on particle velocity; iv) comparing the experimental results with estimations obtained by some particle fallout models available in literature. Our results shows that there is a good agreement between experimental terminal velocities and those obtained applying a model which includes information on particle shape. The proposed methodology allows us to investigate how the particle shape affects the sedimentation processes. Since the latter is strategic to improve the accuracy on modeling ash fallout, this work will contribute to reduce risks to aviations during explosive eruptions.

This paper presents the development and uncertainty characterization of a system for the direct measurement of heat transfer in cooling lava. The system continuously measures the parameters involved in the cooling process and, particularly, in the formation of the crust. The aim is to allow the future development of a physical model of the cooling process itself. In order to realize a system that will be effective in such a hostile environment, the principles on which the instruments for radiation thermometry are based have been thoroughly investigated. A virtual instrument has been developed, interfacing the measuring system and the user, processing the incoming data, and producing an estimate of the uncertainty of the measurement chain. The various sources of uncertainty have been taken into account to produce an accurate estimate of the uncertainty associated with the measured data. The results of experimental tests are presented.

Monitoring is mandatory in hazardous areas with active volcanoes. South Italy countries are seriously affected by continuous risk of volcanic ash emissions, explosions and eruptions. In particular, Mt. Etna and Stromboli volcanoes requires great efforts by the scientific community to develop efficient monitoring systems. Several tasks in surveillance are already being managed by authority; anyway, innovative techniques are required to improve the performance of the monitoring task which is mandatory to improve the quality of public safety. Synergy between competence on geophysical and engineer disciplines must be reinforced to develop monitoring systems aimed to increase information needed to promptly manage crisis arising from volcanic activities. In this paper an innovative monitoring system is proposed. The system is based on smart virtual sensors, which assures real-time management of the volcanic activity by providing a large amount of information on the time evolution of the observed phenomena. The system performs in two parallel tasks: the first aims to optimise the amount of the recorded data by a smart real-time processing of the gathered images and gives very rough set of information on the ongoing events (explosions, eruptions, ash emissions, etc.), while the second task is aimed to a deep analysis of the recorded data to compute statistic indexes characterizing the evolution of the volcanic activity observed (area, height, width, aspect ratio, magma flow rate, etc.). The prototype of the proposed system is composed by a thermal camera sensor and a PC-based environment for data analysis. In particular, two computation tools were developed in the LabVIEW environment which provide the above mentioned information on the ongoing activity along with a smart processing of the acquired data. The system will be aimed to give useful information to the surveillance authority which, on the basis of the obtained results, can suitably manage the incoming events.

In this paper, a novel methodology to measure trajectory and terminal velocity of volcanic ash in laboratory is presented. The methodology consists of the following: 1) planning a lab-scale experiment in order to reproduce the sedimentation processes of fine volcanic ash based on the principle of dynamic similarity; 2) realizing the experimental setup using a glass tank filled with glycerine, a webcam-based vision system and a dedicated image postprocessing tool able to estimate the position and the terminal velocity of any particle falling in the tank; 3) performing a calibration procedure to accurately estimate the uncertainty on particle velocity; and 4) comparing the experimental results with estimations obtained by some particle fallout models available in literature. Our results show that there is a good agreement between experimental terminal velocities and those obtained applying a model which includes information on particle shape. The proposed methodology allows us to investigate how the particle shape affects the sedimentation processes. Since the latter is strategic to improve the accuracy on modeling ash fallout, this work will contribute to reduce risks to aviations during explosive eruptions.