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  • 04. Solid Earth::04.04. Geology::04.04.09. Structural geology  (29)
  • 04.06. Seismology  (27)
  • Creep observations and analysis
  • Elsevier Science Limited  (18)
  • Elsevier B.V.  (12)
  • Nature PG  (11)
  • Istituto Nazionale di Geofisica e Vulcanologia  (7)
  • Wiley  (6)
  • EGU - Copernicus
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  • 1
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    Statistical moments of power spectrum: a fast tool for the classification of seismic events recorded on volcanoes (2021)
    EGU - Copernicus
    Details
    Publikationsdatum: 2021-01-22
    Beschreibung: Spectral analysis has been applied to almost thou-sand seismic events recorded at Vesuvius volcano (Naples,southern Italy) in 2018 with the aim to test a new tool fora fast event classification. We computed two spectral pa-rameters, central frequency and shape factor, from the spec-tral moments of order 0, 1, and 2, for each event at sevenseismic stations taking the mean among the three compo-nents of ground motion. The analyzed events consist ofvolcano-tectonic earthquakes, low frequency events and un-classified events (landslides, rockfall, thunders, quarry blasts,etc.). Most of them are of low magnitude, and/or low maxi-mum signal amplitude, therefore the signal to noise ratio isvery different between the low noise summit stations andthe higher noise stations installed at low elevation aroundthe volcano. The results of our analysis show that volcano-tectonic earthquakes and low frequency events are easily dis-tinguishable through the spectral moments values, particu-larly at seismic stations closer to the epicenter. On the con-trary, unclassified events show the spectral parameters valuesdistributed in a broad range which overlap both the volcano-tectonic earthquakes and the low frequency events. Since thecomputation of spectral parameters is extremely easy and fastfor a detected event, it may become an effective tool for eventclassification in observatory practice.
    Beschreibung: Published
    Beschreibung: 67–74
    Beschreibung: 1SR TERREMOTI - Sorveglianza Sismica e Allerta Tsunami
    Beschreibung: N/A or not JCR
    Schlagwort(e): Vesuvius ; Spectral Analisys ; 04.06. Seismology
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
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    Supervised learning (2021)
    Elsevier B.V.
    Details
    Publikationsdatum: 2021-02-01
    Beschreibung: In supervised classification, we search criteria allowing us to decide whether a sample belongs to a certain class of patterns. The identification of such decision functions is based on examples where we know a priori to which class they belong. The distinction of seismic signals, produced from earthquakes and nuclear explosions, is a classical problem of discrimination using classification with supervision. We move on from observed data—signals originating from known earthquakes and nuclear tests—and search for criteria on how to assign a class to a signal of unknown origin. We begin with Principal Component Analysis (PCA) and Fisher's Linear Discriminant Analysis (FLDA), identifying a linear element separating groups at best. PCA, FLDA, and likelihood-based approaches make use of statistical properties of the groups. Considering only the number of misclassified samples as a cost, we may prefer alternatives, such as the Multilayer Perceptrons (MLPs). The Support Vector Machines (SVMs) use a modified cost function, combining the criterion of the minimum number of misclassified samples with a request of separating the hulls of the groups with a margin as wide as possible. Both SVMs and MLPs overcome the limits of linear discrimination. A famous example for the advantages of the two techniques is the eXclusive OR (XOR) problem, where we wish to form classes of objects having the same parity—even, e.g., (0,0), (1,1) or odd, e.g., (0,1), (1,0). MLPs and SVMs offer effective methods for the identification of nonlinear decision functions, allowing us to resolve classification problems of any complexity provided the data set used during earning is sufficiently large. In Hidden Markov Models (HMMs), we consider observations where their meaning depends on their context. Observations form a causal chain generated by a hidden process. In Bayesian Networks (BNs) we represent conditional (in)dependencies between a set of random variables by a graphical model. In both HMMs and BNs, we aim at identifying models and parameters that explain observations with a highest possible degree of probability.
    Beschreibung: Published
    Beschreibung: 33-85
    Beschreibung: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Schlagwort(e): pattern recognition ; supervised learning ; Support Vector Machines ; Multilayer Perceptrons ; Hidden Markov Models ; Bayesian Networks ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: book chapter
    Standort Signatur Erwartet Verfügbarkeit
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  • 3
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    Patterns, objects, and features (2021)
    Elsevier B.V.
    Details
    Publikationsdatum: 2021-02-01
    Beschreibung: Patterns and objects are described by a variety of characteristics, namely features and feature vectors. Features can be numerical, ordinal, and categorical. Patterns can be made up of a number of objects, such as in speech processing. In geophysics, numerical features are the most common ones and we focus on those. The choice of appropriate features requires a priori reasoning about the physical relation between patterns and features. We present strategies for feature identification and procedures suitable for pattern recognition. In time series analysis and image processing, the direct use of raw data is not feasible. Procedures of feature extraction, based on locally encountered characteristics of the data, are applied. Here we present the problem of delineating segments of interest in time series and textures in image processing. In transformations, we “translate” our raw data to a form suitable for learning. In Principal Component Analysis, we rotate the original features to a system of uncorrelated variables, limiting redundancy. Independent Component Analysis follows a similar strategy, transforming our data into variables independent of each other. Fourier transform and wavelet transform are based on the representation of the original data as a series of basis functions—sines and cosines or finite-length wavelets. Redundancy reduction is achieved considering the contributions of the single basis functions. Even though a large number of features help to solve a classification problem, feature vectors with high dimensions pose severe problems. Besides the computational burden, we encounter problems known under the term “curse of dimensionality.” The curse of dimensionality entails the necessity of feature selection and reduction, which includes a priori considerations as well as redundancy reduction. The significance of features may be evaluated with tests, such as Student’s t or Hotelling's T2, and, in more complex problems, with cross-validation methods.
    Beschreibung: Published
    Beschreibung: 3-13
    Beschreibung: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Schlagwort(e): pattern recognition ; objects ; features ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: book chapter
    Standort Signatur Erwartet Verfügbarkeit
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  • 4
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    Stress releasemodel and proxy measures of earthquake size. Application to Italian seismogenic sources (2016)
    Elsevier Science Limited
    Details
    Publikationsdatum: 2021-05-12
    Beschreibung: This study presents a series of self-correcting models that are obtained by integrating information about seismicity and fault sources in Italy. Four versions of the stress release model are analyzed, in which the evolution of the system over time is represented by the level of strain, moment, seismic energy, or energy scaled by the moment. We carry out the analysis on a regional basis by subdividing the study area into eight tectonically coherent regions. In each region, we reconstruct the seismic history and statistically evaluate the completeness of the resulting seismic catalog. Following the Bayesian paradigm, we apply Markov chain Monte Carlo methods to obtain parameter estimates and a measure of their uncertainty expressed by the simulated posterior distribution. The comparison of the four models through the Bayes factor and an information criterion provides evidence (to different degrees depending on the region) in favor of the stress release model based on the energy and the scaled energy. Therefore, among the quantities considered, this turns out to be the measure of the size of an earthquake to use in stress release models. At any instant, the time to the next event turns out to follow a Gompertz distribution, with a shape parameter that depends on time through the value of the conditional intensity at that instant. In light of this result, the issue of forecasting is tackled through both retrospective and prospective approaches. Retrospectively, the forecasting procedure is carried out on the occurrence times of the events recorded in each region, to determine whether the stress release model reproduces the observations used in the estimation procedure. Prospectively, the estimates of the time to the next event are compared with the dates of the earthquakes that occurred after the end of the learning catalog, in the 2003–2012 decade.
    Beschreibung: Italian Dipartimento della Protezione Civile in the framework of the 2007–2009 Agreement with Istituto Nazionale di Geofisica e Vulcanologia (INGV), project S1: Analysis of the seismic potential in Italy for the evaluation of the seismic hazard.
    Beschreibung: Published
    Beschreibung: 147-168
    Beschreibung: 2T. Tettonica attiva
    Beschreibung: 3T. Pericolosità sismica e contributo alla definizione del rischio
    Beschreibung: JCR Journal
    Beschreibung: restricted
    Schlagwort(e): point process ; probabilistic forecasting ; interevent time distribution ; seismogenic sources ; Bayesian inference ; 04. Solid Earth::04.04. Geology::04.04.01. Earthquake geology and paleoseismology ; 04. Solid Earth::04.04. Geology::04.04.09. Structural geology ; 04. Solid Earth::04.06. Seismology::04.06.02. Earthquake interactions and probability ; 04. Solid Earth::04.06. Seismology::04.06.11. Seismic risk ; 04. Solid Earth::04.07. Tectonophysics::04.07.07. Tectonics ; 05. General::05.01. Computational geophysics::05.01.04. Statistical analysis
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
    Standort Signatur Erwartet Verfügbarkeit
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  • 5
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    Images of ancient Calabrian-Sicilian earthquakes from a stereoscopic viewer of the early 20th century. The ethics behind a natural disaster photo-gallery (2021)
    Istituto Nazionale di Geofisica e Vulcanologia
    Details
    Publikationsdatum: 2021-05-12
    Beschreibung: This research was inspired by an old stereoscopic viewer from the early 1900s, containing 42 glass slides depicting scenes from two Italian earthquakes that struck Southern Calabria and Eastern Sicily in the years 1894 and 1905, causing hundreds of deaths, but whose memory was blurred by the subsequent, great earthquake of the Messina Straits of December 28, 1908. The sequence of three-dimensional images shown by the viewer gave a deep and realistic visual impact to scenes of collapses, debris, and victims, arousing feelings of dismay. In this work, we describe the viewer apparatus; the places depicted in the stereoscopic plates, and the seismic phenomena that caused the disasters. But above all, we investigate the social and cultural aims that pushed to show the effects of local earthquakes through this kind of primitive multimedia mechanism. We exclude that the viewer, with its photographic equipment, was merely an instrument of entertainment. We rather assume that it carried out an educational task. The repetition of the sequence of tragic images of earthquakes through the stereoscopic viewer had the purpose of contributing to give awareness of the looming seismic risk and to accept rationally those recurring disasters.
    Beschreibung: Published
    Beschreibung: Se104
    Beschreibung: 4T. Sismicità dell'Italia
    Beschreibung: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Beschreibung: 1TM. Formazione
    Beschreibung: 2TM. Divulgazione Scientifica
    Beschreibung: JCR Journal
    Schlagwort(e): Stereoscopic Viewer ; Calabrian-Sicilian Earthquakes ; Observational Seismology ; Seismic-Risk ; Geo-Education ; Geoethics ; 04.06. Seismology ; 05.03. Educational, History of Science, Public Issues ; 05.04. Instrumentation and techniques of general interest
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
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  • 6
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    The growth and erosion of cinder cones in Guatemala and El Salvador: Models and statistics (2011)
    Elsevier B.V.
    Details
    Publikationsdatum: 2020-11-18
    Beschreibung: Morphologic data for 147 cinder cones in southern Guatemala andwestern El Salvador are comparedwith data from the San Francisco volcanic field, Arizona (USA), Cima volcanic field, California (USA), Michoácan–Guanajuato volcanic field, Mexico, and the Lamongan volcanic field, East Java. The Guatemala cones have an average height of 110+/-50 m, an average basal diameter of 660+/-230 m and an average top diameter of 180+/-150 m. The generalmorphology of these cones can be described by their average cone angle of slope (24+/-7), average heightto- radius ratio (0.33+/-0.09) and their flatness (0.24+/-0.18). Although the mean values for the Guatemalan cones are similar to those for other volcanic fields (e.g., San Francisco volcanic field, Arizona; Cima volcanic field, California; Michoácan–Guanajuato volcanic field, Mexico; and Lamongan volcanic field, East Java), the range of morphologies encompasses almost all of those observed worldwide for cinder cones. Three new 40Ar/39Ar age dates are combined with 19 previously published dates for cones in Guatemala and El Salvador. There is no indication that the morphologies of these cones have changed over the last 500–1000 ka. Furthermore, a re-analysis of published data for other volcanic fields suggests that only in the Cima volcanic field (of those studied) is there clear evidence of degradation with age. Preliminary results of a numerical model of cinder cone growth are used to show that the range of morphologies observed in the Guatemalan cinder cones could all be primary, that is, due to processes occurring at the time of eruption.
    Beschreibung: Support for Walker was provided by NSF MARGINS grant OCE- 0405666.
    Beschreibung: Published
    Beschreibung: 39-52
    Beschreibung: 1.5. TTC - Sorveglianza dell'attività eruttiva dei vulcani
    Beschreibung: 3.5. Geologia e storia dei vulcani ed evoluzione dei magmi
    Beschreibung: 3.6. Fisica del vulcanismo
    Beschreibung: JCR Journal
    Beschreibung: open
    Schlagwort(e): cinder cones ; morphology ; age dating ; 04. Solid Earth::04.01. Earth Interior::04.01.99. General or miscellaneous ; 04. Solid Earth::04.04. Geology::04.04.99. General or miscellaneous ; 04. Solid Earth::04.04. Geology::04.04.03. Geomorphology ; 04. Solid Earth::04.04. Geology::04.04.09. Structural geology ; 04. Solid Earth::04.08. Volcanology::04.08.99. General or miscellaneous ; 05. General::05.02. Data dissemination::05.02.03. Volcanic eruptions
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
    Standort Signatur Erwartet Verfügbarkeit
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  • 7
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    A posteriori analyses—advantages and pitfalls of pattern recognition techniques (2021)
    Elsevier B.V.
    Details
    Publikationsdatum: 2021-02-01
    Beschreibung: In this chapter, we deal with a posterior analysis of supervised and unsupervised learning techniques. Concerning supervised learning, we discuss methods of cross-validation and assessment of uncertainty of tests by means of the “Receiver Operation Curve” and the “Kappa-Statistics.” We show the importance of appropriate target information. Furthermore, features are critical; when they are not properly chosen, they fail to describe objects in a unique way. A critical attitude is mandatory to validate the success of an application. A high score of success does not automatically mean that a method is truly effective. At the same time, users should not despair when the desired success is not achieved. A posteriori analysis on the reasons for an apparent failure may provide useful insights into the problem. Targets may not be appropriately defined, features can be inadequate, etc. Problems can be often fixed by adjusting a few choices; sometimes a change of strategy may be necessary to improve results. In unsupervised learning, we ask whether the structures revealed in the data are meaningful. Cluster analysis offers rules giving formal answers to this question; however, such rules are not generally applicable. In some cases, a heuristic approach may be necessary.
    Beschreibung: Published
    Beschreibung: 237-259
    Beschreibung: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Schlagwort(e): pattern recognition ; a posteriori analysis ; supervised learning ; unsupervised learning ; cross validation ; assessment of uncertainty ; Receiver Operation Curve ; Kappa-Statistics ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: book chapter
    Standort Signatur Erwartet Verfügbarkeit
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  • 8
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    Unsupervised learning (2021)
    Elsevier B.V.
    Details
    Publikationsdatum: 2021-02-01
    Beschreibung: Unsupervised learning is based on the definition of an appropriate metrics defining the similarity of patterns. On the basis of the metrics, we form groups or clusters of patterns following various strategies. In partitioning cluster analysis, we form disjoint clusters. Being faced with data, where clusters still exhibit heterogeneities or subclusters, we may adopt the strategy of hierarchical clustering, which leads to the generation of the so-called dendrograms. In the partitioning strategy, we choose a priori the number of clusters we wish to form, whereas in the hierarchical strategy, the number of clusters depends on the resolution we want to have. Density-based clustering considers local structures of a data set. We consider a unit volume in our data space and derive the density of samples within this volume. Moving toward neighboring volumes, we verify whether the number of samples has dropped below a threshold. If this is the case, we identify a heterogeneity, otherwise we join the neighboring volumes to a common cluster. Self-Organizing Maps (SOMs) provide a way of representing multidimensional data in much lower dimensional spaces than the original data set. The process of reducing the dimensionality of vectors is essentially a data compression technique known as vector quantization. The SOM technique creates a network that stores information in a way that it maintains the topological relationships within the patterns of the data set. Each node of the network represents a number of patterns. Assigning a color code to the nodes, the representation of pattern characteristics with high-dimensional feature vectors becomes extremely effective.
    Beschreibung: Published
    Beschreibung: 87-124
    Beschreibung: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Schlagwort(e): pattern recognition ; unsupervised learning ; cluster analysis ; Density-based clustering ; Self-Organizing Maps ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: book chapter
    Standort Signatur Erwartet Verfügbarkeit
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  • 9
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    Applications with unsupervised learning (2021)
    Elsevier B.V.
    Details
    Publikationsdatum: 2021-02-01
    Beschreibung: This chapter demonstrates how Unsupervised Learning can be applied in Geophysics. It starts with an example of clustering seismic spectra obtained on Stromboli volcano. K-means clustering as well as clustering using the Adaptive Criterion are applied. The latter criterion is preferred as it better matches the statistical characteristics of the data. Clusters show close relation to the state of volcanic activity. Density based clustering reveals groups whose hulls can be of irregular shape. This makes the method attractive, among others, for the identification of structural elements in geology, which often do not have a simple geometry. An example application is discussed considering the distribution of earthquake locations on Mt Etna, which clearly evidence structures already identified by other, independent evidences. Using SOM we aim at data reduction and effective graphical visualization. In an example for climate data we demonstrate the application of SOM for zoning purposes. Besides, the temporal evolution of spectral seismic data recorded on Mt Etna can be effectively monitored using SOM. We further illustrate the use of SOM for directional data, which can be handled best using a toroidal sheet geometry. We discuss this using a data set of seismic moment tensors of Mediterranean earthquakes.
    Beschreibung: Published
    Beschreibung: 189-234
    Beschreibung: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Schlagwort(e): pattern recognition ; unsupervised learning ; Density based clustering ; Stromboli ; earthquakes ; volcanic activity ; structural data ; seismic moment tensors ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: book chapter
    Standort Signatur Erwartet Verfügbarkeit
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  • 10
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    Software manuals (2021)
    Elsevier B.V.
    Details
    Publikationsdatum: 2021-02-01
    Beschreibung: In this chapter, we present scripts and programs that accompany this book. Five MATLAB scripts regard simple examples related to supervised learning, that is, linear discrimination, the perceptron, support vector machines, and hidden Markov models. Seven scripts are devoted to unsupervised learning, such as K-means and fuzzy clustering, agglomerative clustering, density-based clustering, and clustering of patterns where features are correlated. These scripts provide a starting point for the reader, who can adjust and modify the codes with respect to proper needs. Besides, we provide sources and executables of programs that can be readily applied to larger and more complex datasets. These programs regard supervised learning using multilayerperceptron and support vector machines. KKAnalysis is a toolbox for unsupervised learning and offers various options of clustering and the use of self-organizing maps. The programs offer graphical user interfaces (GUI) to facilitate their use and create both graphical and alphanumeric output that can be used in further processing steps. The programs come along with real-world datasets that are also discussed in the example applications presented in various chapters of the book. Other propaedeutic material can be found in a folder called “miscellaneous.”
    Beschreibung: Published
    Beschreibung: 261-313
    Beschreibung: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Schlagwort(e): pattern recognition ; software manuals ; MATLAB scripts ; 04.04. Geology ; 04.06. Seismology ; 04.07. Tectonophysics ; 04.08. Volcanology ; 05.04. Instrumentation and techniques of general interest
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: book chapter
    Standort Signatur Erwartet Verfügbarkeit
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