http://hdl.handle.net/2122/273 Tue, 21 May 2013 06:47:11 GMT 2013-05-21T06:47:11Z http://hdl.handle.net/2122/8492 Title: Depth estimation of cavities from microgravity data using a new approach: the local linear model tree (LOLIMOT) Authors: Hajian, A.; Department of Geophysics, Science and Research Branch, Islamic Azad University, Tehran, Iran; Zomorrodian, H.; Department of Geophysics, Science and Research Branch, Islamic Azad University, Tehran, Iran; Styles, P.; Applied and Environmental Geophysics Group, Keele University, Keele ST5 5BG, UK; Greco, F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Lucas, C.; Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran, Iran Abstract: In this paper an attempt is made to estimate depth and shape parameters of subsurface cavities from microgravity data through a new soft computing approach: the locally linear model tree, known as the LOLIMOT algorithm. This method is based on locally linear neuro-fuzzy modelling, which has recently played a successful role in various applications over non-linear system identification. A multiple-LOLIMOT neuro-fuzzy model was trained separately for each of the three most common shapes of subsurface cavities: sphere, vertical cylinder and horizontal cylinder. The method was then tested for each of the cavity shapes with synthetic data. The model’s suitability for application to real cases was analysed by adding random Gaussian noise to the data to simulate several levels of uncertainty and the results of LOLIMOT were compared to both multi-layer perceptron neural network and leastsquares minimization methods. The results showed that the LOLIMOT algorithm is more robust to noise and is also more precise than either the multi-layer perceptron or least-squares minimization method. Furthermore, the method was tested with microgravity data over a selected site located in a major container terminal at Freeport, Grand Bahamas, to estimate cavity depth and was compared to the results achieved by least-squares minimization and multi-layer perceptron methods. The proposed method can estimate cavity parameters more accurately than the least-squares minimization and multi-layer perceptron methods. Thu, 31 May 2012 22:00:00 GMT http://hdl.handle.net/2122/8492 2012-05-31T22:00:00Z http://hdl.handle.net/2122/7911 Title: Testing the performance of some nonparametric pattern recognition algorithms in realistic cases Authors: Sandri, L.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia; Marzocchi, W.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia Abstract: The success obtained by Statistical Pattern Recognition in many disciplines is certainly related to the quality and availability of many data, normally distributed. However, in other disciplines, the data sets consist of few measurements, often binned, correlated, and not normally distributed. Usually, we do not even know which features have an influence on the process. The main goal of this paper is to evaluate the performance of some nonparametric Pattern Recognition algorithms when applied to such data. Finally we show the results of the application of the four nonparametric statistical pattern recognition techniques to real volcanological data. Sun, 29 Feb 2004 23:00:00 GMT http://hdl.handle.net/2122/7911 2004-02-29T23:00:00Z http://hdl.handle.net/2122/7761 Title: KSBT: A Knowledge-based Systems Building Tool Authors: Bruno, Silvia; Dip. di Ingegneria Strutturale "La Sapienza"; Gavarini, Carlo; Dip. di Ingegneria Strutturale "La Sapienza"; Padula, Antonio; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia; Vittori, Federico; Dip. di Ingegneria Strutturale "La Sapienza" Abstract: The increasing interest in AI as a powerful aid in solving civil engineering problems has suggested the realization of a domain-independent tool for knowledge-based systems construction. The paper provides the description of a fuzzy inference engine, which has appropriately been developed in order to both build knowledge bases and to perform evaluations. Knowledge acquisition issues and approximate reasoning techniques are also illustrated and discussed. Wed, 15 Mar 1995 23:00:00 GMT http://hdl.handle.net/2122/7761 1995-03-15T23:00:00Z http://hdl.handle.net/2122/7753 Title: Applicazioni di Intelligenza Artificiale alla difesa dai terremoti Authors: Padula, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia; Gavarini, C.; Dip. di Ingegneria Strutturale "La Sapienza" Editors: Bernardini, Alberto; Università degli Studi di Padova Abstract: During the last ten years the Research Unit has carried on its investigations relative to the utilisation of AI methodologies for seismic protection. Interesting results have been obtained, due to the possibilities the same methodologies offer to: • Supply operative tools for activities on field, in general Expert Systems; • Supply a user friendly environment, also dealing with large amount of information coming from various sources: survey and evaluation of data, decisions, design provisions, priorities, code specifications…; • Manage uncertain data and/or heuristic or empirical information, as well as to deal with semantic value of linguistic sources; The research results have been illustrated in the works indicated in the bibliography. Seismic risk is the common connotation unifying the different questions dealt with. Further applications in the frame of Civil Engineering and Architecture are possible, too. The investigations regard: • Priority choices in interventions on monuments under seismic risk. The proposed tool: Expert System EXPRIM • Utilisation of "Fuzzy Set" theory for the interpretation of historical data regarding seismic damages • Postearthquake usability assessment of buildings damaged by an earthquake or by a seismic crisis. The proposed tool: Expert System AMADEUS • Definition of an Informative System and of a Knowledge Based System for monuments and/or historical building heritage that should guide in data supply and management, as well as in safeguard and risk mitigation interventions. In the course of the investigation different kinds of informatics tools have been used, such as normal programming languages, PROLOG and the shell NEXPERT OBJECT. The exigency to have at disposal a tool able to use the Fuzzy logic, as well as the problems inherent to the same investigations has led the Research Unit to set up an original shell called KSBT. Fri, 31 Dec 1999 23:00:00 GMT http://hdl.handle.net/2122/7753 1999-12-31T23:00:00Z http://hdl.handle.net/2122/7665 Title: Implementazione di una nuova procedura per caratterizzare la forma di particelle mediante misure al CAMSIZER e algoritmi di clustering Authors: Lo Castro, M. D.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Andronico, D.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Cassisi, C.; Università degli Studi di Catania; Montalto, P.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Prestifilippo, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia Abstract: In this work we present the calibration phase of a new procedure for the characterization of the shape of pyroclastic particles. This research has been granted by INGV of Catania, with funds deriving from the “Progetto Giovani”, in collaboration with Retsch Technology in Haan. The innovation of this procedure arises from the use of CAMSIZER (an instrument developed by the German leader company). This instrument permits to obtain very important information both on size and shape parameters of a high number of particles (hundreds of thousands data). Moreover, we used clustering and classification algorithms in order to group particles according to their morphologic characteristics. This calibration phase has been tested only on standard materials with regular geometries such as cubes, spheres and cylinders. In the future we will apply this methodology to volcanic ash particles that, as well-known, are characterized by irregular morphologies. Fri, 31 Dec 2010 23:00:00 GMT http://hdl.handle.net/2122/7665 2010-12-31T23:00:00Z http://hdl.handle.net/2122/7645 Title: Pattern recognition of volcanic tremor data on Mt. Etna (Italy) with KKAnalysis — A software program for unsupervised classification Authors: Messina, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia; Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia Abstract: Continuous seismic monitoring plays a key role in the surveillance of the Mt. Etna volcano. Besides earthquakes, which often herald eruptive episodes, the persistent background signal, known as volcanic tremor, provides important information on the volcano status. Changes in the regimes of activity are usually concurrent with variations in tremor characteristics. As continuous recording leads rapidly to the accumulation of large amounts of data, parameter extraction and automated processing become crucial. We propose techniques of unsupervised classification and present a software, named KKAnalysis, developed for this purpose. Essentials of KKAnalysis are demonstrated on tremor data recorded on Mt. Etna during various states of volcanic activity encountered in 2007 and 2008. KKAnalysis is based on MATLAB and combines various unsupervised pattern recognition techniques, in particular self-organizing maps (SOM) and cluster analysis. An early software version was successfully applied to seismic signals recorded on Mt. Etna during the eruption in 2001. Since each situation may require different configurations, we designed KKAnalysis with a specific GUI allowing users to easily modify parameters. All results are given graphically, in screen plots and metafiles (MATLAB and TIF format), as well as in alphanumeric form. The synoptic visualization of results from SOM and cluster analysis facilitates an immediate inspection. The potential of this representation is demonstrated by focusing on data recorded during a flank eruption on May 13, 2008. Changes of tremor characteristics can be clearly identified at a very early stage, well before enhanced volcanic activity becomes visible in the time series. At the same time, data reduction to less than 1% of the original amount is achieved, which facilitates interpretation and storage of the essential information. Running the program in a typical configuration requires computing time less than 1 min, allowing an on-line application for early warning purposes at INGV–Sezione di Catania Sat, 26 Mar 2011 23:00:00 GMT http://hdl.handle.net/2122/7645 2011-03-26T23:00:00Z http://hdl.handle.net/2122/7274 Title: Clustering of Hybrid Events at Stromboli Volcano (Italy) Authors: Esposito, A.M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia Editors: Apolloni, B.; Department of Computer Science University of Milano, Italy; Bassis, S.; Department of Computer Science University of Milano, Italy; Esposito, A.; Dep. of Psychology, Second University of Naples, Caserta, Italy; Morabito, C.F.; Università Mediterranea di Reggio Calabria Abstract: The last effusive eruption on February 27, 2007 at Stromboli volcano was characterized by the occurrence of a particular typology of seismic events named “hybrids”. During March about 4000 of these signals were recorded, and three main swarms happened: the first one on days 6-8, with more than 1200 events; the second one on day 20, with more than 400 events; and the third one on day 22, with about 600 events. The study of these events and specifically their location is the main purpose of this work because it not only characterizes a particular aspect of the 2007 effusive eruption but at the same time can improve the understanding of the eruptive processes of the volcano. Thus, in order to locate them it was first necessary to group the signals according to their waveform similarity and then apply relative location techniques on individual families. To perform the clustering an unsupervised SOM neural network was used. This technique is capable of working without any “a-priori” information about data distribution and structure. Its results have revealed differences in the families of events recorded during and between the swarms, underlying from a volcanological point different locations or source mechanisms of the involved structures. Moreover, they have shown to be consistent compared to those obtained by applying the Hierarchical Clustering technique. However, in contrast to the latter, the SOM clustering does not critically depend on its parameters and allows for an easier result visualization and interpretation. Thu, 02 Jun 2011 22:00:00 GMT http://hdl.handle.net/2122/7274 2011-06-02T22:00:00Z http://hdl.handle.net/2122/7267 Title: Defining high-detail hazard maps by a cellular automata approach: application to Mount Etna (Italy) Authors: Rongo, R.; Università della Calabria, Dipartimento di Scienze della Terra, Arcavacata di Rende (Cosenza), Italy; Avolio, M. A.; Università della Calabria, Dipartimento di Matematica, Arcavacata di Rende (Cosenza), Italy; Behncke, B.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; D'Ambrosio, D.; Università della Calabria, Dipartimento di Matematica, Arcavacata di Rende (Cosenza), Italy; Di Gregorio, S.; Università della Calabria, Dipartimento di Matematica, Arcavacata di Rende (Cosenza), Italy; Lupiano, V.; Università della Calabria, Dipartimento di Scienze della Terra, Arcavacata di Rende (Cosenza), Italy; Neri, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Spataro, W.; Università della Calabria, Dipartimento di Matematica, Arcavacata di Rende (Cosenza), Italy; Crisci, G. M.; Università della Calabria, Dipartimento di Scienze della Terra, Arcavacata di Rende (Cosenza), Italy Abstract: The individuation of areas that are more likely to be affected by new events in volcanic regions is of fundamental relevance for the mitigation of the possible consequences, both in terms of loss of human life and material properties. Here, we describe a methodology for defining flexible high-detail lava-hazard maps and a technique for the validation of the results obtained. The methodology relies on: (i) an accurate analysis of the past behavior of the volcano; (ii) a new version of the SCIARA model for lava-flow simulation (based on the macroscopic cellular automata paradigm); and (iii) high-performance parallel computing for increasing computational efficiency. The new release of the SCIARA model introduces a Bingham-like rheology as part of the minimization algorithm of the differences for the determination of outflows from a generic cell, and an improved approach to lava cooling. The method is here applied to Mount Etna, the most active volcano in Europe, and applications to landuse planning and hazard mitigation are presented. Wed, 30 Nov 2011 23:00:00 GMT http://hdl.handle.net/2122/7267 2011-11-30T23:00:00Z http://hdl.handle.net/2122/7258 Title: Near‐real‐time forecasting of lava flow hazards during the 12–13 January 2011 Etna eruption Authors: Vicari, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Ganci, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Behncke, B.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Cappello, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Neri, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Del Negro, C.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia Abstract: Forecasting the lava flow invasion hazard in near‐real time is a primary challenge for volcano monitoring systems. The paroxysmal episode at Mount Etna on 12–13 January 2011 produced in ∼4 hours lava fountains and fast‐moving lava flows 4.3 km long. We produced timely predictions of the areas likely to be inundated by lava flows while the eruption was still ongoing. We employed infrared satellite data (MODIS, AVHRR, SEVIRI) to estimate in near‐realtime lava eruption rates (peak value of 60 m3 s−1). These time‐varying discharge rates were then used to drive MAGFLOW simulations to chart the spread of lava as a function of time. Based on a classification on durations and lava volumes of ∼130 paroxysms at Etna in the past 13 years, and on lava flow path simulations of expected eruptions, we constructed a lava flow invasion hazard map for summit eruptions, providing a rapid response to the impending hazard. This allowed key at‐risk areas to be rapidly and appropriately identified. Wed, 06 Jul 2011 22:00:00 GMT http://hdl.handle.net/2122/7258 2011-07-06T22:00:00Z http://hdl.handle.net/2122/6997 Title: A neural network approach using multi-scale textural metrics from very high resolution panchromatic imagery for urban land-use classification Authors: Pacifici, F.; Tor Vergata University; Chini, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia; Emery, W. J.; Colorado University Abstract: The successful launch of panchromatic WorldView-1 and the planned launch of WorldView-2 will make a major contribution towards the advancement of the commercial remote sensing industry by providing improved capabilities, more frequent revisits and greater imaging flexibility with respect to the precursor QuickBird satellite. Remote sensing data from panchromatic systems have a potential for more detailed and accurate mapping of the urban environment with details of sub-meter ground resolution, but at the same time, they present additional complexities for information mining. In this study, very high-resolution panchromatic images from QuickBird and WorldView-1 have been used to accurately classify the land-use of four different urban environments: Las Vegas (U.S.A.), Rome (Italy), Washington D.C. (U.S.A.) and San Francisco (U.S.A.). The proposed method is based on the analysis of firstand second-order multi-scale textural features extracted from panchromatic data. For this purpose, textural parameters have been systematically investigated by computing the features over five different window sizes, three different directions and two different cell shifts for a total of 191 input features. Neural Network Pruning and saliency measurements made it possible to determine the most important textural features for sub-metric spatial resolution imagery of urban scenes. The results show that with a multi-scale approach it is possible to discriminate different asphalt surfaces, such as roads, highways and parking lots due to the different textural information content. This approach also makes it possible to differentiate building architectures, sizes and heights, such as residential houses, apartment blocks and towers with classification accuracies above 0.90 in terms of Kappa coefficient computed over more than a million independent validation pixels. Sun, 31 May 2009 22:00:00 GMT http://hdl.handle.net/2122/6997 2009-05-31T22:00:00Z