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  • JSTOR Archive Collection Business II  (63)
  • 04.06. Seismology  (13)
  • Volcano seismology
  • JSTOR  (63)
  • Wiley  (8)
  • Elsevier B.V.  (7)
  • Egu-Copernicus  (4)
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  • 1
    Publikationsdatum: 2024-02-07
    Beschreibung: A catalogue of precisely located micro-seismicity is fundamental for investigating seismicity and rock physical properties in active tectonic and volcanic regions and for the definition of a ‘baseline’ seismicity, required for a safe future exploitation of georesource areas. In this study, we produce the first manually revised catalogue of micro-seismicity for Co. Donegal region (Ireland), an area of about 50K M2 of on-going deformation, aimed at localizing natural micro-seismic events occurred between 2012 and 2015. We develop a stochastic method based on a Markov chain Monte Carlo (McMC) sampling approach to compute earthquake hypocentral location parameters. Our results indicates that micro-seismicity is present with magnitudes lower than 2 (the highest magnitude is 2.8).The recorded seismicity is almost clustered along previously mapped NE-SW trending, steeply dipping faults and confined within the upper crust (focal depth less than 10 km). We also recorded anthropogenic seismicity mostly related to quarries' activity in the study area.
    Beschreibung: Published
    Beschreibung: 62-76
    Beschreibung: OST1 Alla ricerca dei Motori Geodinamici
    Beschreibung: JCR Journal
    Schlagwort(e): 04.06. Seismology
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
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  • 2
    Publikationsdatum: 2023-08-29
    Beschreibung: A methodology to detect local incompleteness of macroseismic intensity data at the local scale is presented. In particular, the probability that undocumented effects actually occurred at a site is determined by considering intensity prediction equations (in their probabilistic form) integrated by observations relative to known events documented at surrounding sites. The outcomes of this analysis can be used to investigate how representative and known the seismic histories of localities are (i.e., the list of documented effects through time). The proposed approach is applied to the Italian area. The analysis shows that, at most of the considered sites, the effects of intensity ≥ 6 should most probably have occurred at least once, but they are not contained in the current version of the Italian macroseismic databases. In a few cases, instead, the lack of data may concern higher intensity levels (i.e., ≥ 8). The geographical distribution of potentially lost information reflects the heterogeneity of the seismic activity over the Italian territory.
    Beschreibung: Published
    Beschreibung: 1805–1816
    Beschreibung: 4T. Sismicità dell'Italia
    Beschreibung: JCR Journal
    Schlagwort(e): Macroseismology ; 04.06. Seismology
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
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  • 3
    Publikationsdatum: 2023-08-29
    Beschreibung: The Italian historical earthquake record is among the richest worldwide; as such it allows for the development of advanced techniques for retrieving quantitative information by calibration with recent earthquakes. Building on a pilot elaboration of northern Italian earthquakes, we developed a procedure for determining the hypocentral depth of all Italian earthquakes from macroseismic intensity data alone. In a second step the procedure calculates their magnitude, taking into account the inferred depth. Hypocentral depth exhibits substantial variability countrywide but has so far received little attention: pre-instrumental earthquakes were routinely “flattened” at the upper-crustal level (∼10 km), on the grounds that the calculation of hypocentral depth is heavily dependent on the largely unknown local propagation properties. We gathered a learning set of 42 earthquakes documented by reliable instrumental data and by numerous macroseismic intensity observations. We observe (1) that within 50 km from the epicenter the ground motion attenuation rate is primarily controlled by hypocentral depth and largely independent of magnitude, (2) that within this distance the fluctuations in crustal attenuation properties are negligible countrywide, and (3) that knowing both the depth and the expected epicentral intensity makes it possible to estimate a reliable magnitude.
    Beschreibung: INGV DPC, 2019–2021 agreement; All. A, WP 7
    Beschreibung: Published
    Beschreibung: 1007–1028
    Beschreibung: 4T. Sismicità dell'Italia
    Beschreibung: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Beschreibung: JCR Journal
    Schlagwort(e): hypocentral depth ; magnitude ; macroseismology ; pre-instrumental earthquakes ; 04.06. Seismology
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
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  • 4
    Publikationsdatum: 2022-09-06
    Beschreibung: A new probabilistic seismic hazard model, called Modello di Pericolosità Sismica 2019 (MPS19), has been recently proposed for the Italian territory, as a result of the efforts of a large national scientific community. This model is based on 11 groups of earthquake rupture forecast inputs and, particularly, on 5 area-source seismogenic models, including the so-called MA4 model. Data-driven procedures were followed in MA4 to evaluate seismogenic parameters of each area source, such as upper and lower seismogenic depths, hypocentral-depth distributions, and nodal planes. In a few cases, expert judgement or ad hoc assumptions were necessary due to the scarcity of data. MA4 consists of 20 seismicity models that consider epistemic uncertainty in the estimations of the completeness periods of the earthquake catalogue, of maximum magnitude values and of seismicity rates. In particular, five approaches were adopted to calculate the rates, in the form of the truncated Gutenberg–Richter frequency–magnitude distribution. The first approach estimated seismicity rates using earthquakes located in each area source, while the other approaches firstly calculated the seismicity rates for groups of areas considered tectonically homogeneous and successively partitioned in different ways the values to the area forming each group. The results obtained in terms of seismic hazard estimates highlight that the uncertainty explored by the 20 seismicity models of MA4 is at least of the same order of magnitude as the uncertainty due to alternative ground motion models.
    Beschreibung: Published
    Beschreibung: 2807–2827
    Beschreibung: 6T. Studi di pericolosità sismica e da maremoto
    Beschreibung: JCR Journal
    Schlagwort(e): seismogenic model, seismic hazard, Italy ; 04.06. Seismology
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
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  • 5
    Publikationsdatum: 2021-12-15
    Beschreibung: We investigated the seismic fault structure and the rupture characteristics of the MW 6.6, 2 May 2020, Cretan Passage earthquake through tsunami data inverse modelling. Our results suggest a shallow crustal event with a reverse mechanism within the accretionary wedge rather than on the Hellenic Arc subduction interface. The study identifies two possible ruptures: a steeply sloping reverse splay fault and a back-thrust rupture dipping south, with a more prominent dip angle.
    Beschreibung: We present a source solution for the tsunami generated by the Mw 6.6 earthquake that occurred on 2 May 2020, about 80 km offshore south of Crete, in the Cretan Passage, on the shallow portion of the Hellenic Arc subduction zone (HASZ). The tide gauges recorded this local tsunami on the southern coast of Crete and Kasos island. We used Crete tsunami observations to constrain the geometry and orientation of the causative fault, the rupture mechanism, and the slip amount. We first modelled an ensemble of synthetic tsunami waveforms at the tide gauge locations, produced for a range of earthquake parameter values as constrained by some of the available moment tensor solutions. We allow for both a splay and a back-thrust fault, corresponding to the two nodal planes of the moment tensor solution. We then measured the misfit between the synthetic and the Ierapetra observed marigram for each source parameter set. Our results identify the shallow, steeply dipping back-thrust fault as the one producing the lowest misfit to the tsunami data. However, a rupture on a lower angle fault, possibly a splay fault, with a sinistral component due to the oblique convergence on this segment of the HASZ, cannot be completely ruled out. This earthquake reminds us that the uncertainty regarding potential earthquake mechanisms at a specific location remains quite significant. In this case, for example, it is not possible to anticipate if the next event will be one occurring on the subduction interface, on a splay fault, or on a back-thrust, which seems the most likely for the event under investigation. This circumstance bears important consequences because back-thrust and splay faults might enhance the tsunamigenic potential with respect to the subduction interface due to their steeper dip. Then, these results are relevant for tsunami forecasting in the framework of both the long-term hazard assessment and the early warning systems.
    Beschreibung: Published
    Beschreibung: 3713–3730
    Beschreibung: 8T. Sismologia in tempo reale e Early Warning Sismico e da Tsunami
    Beschreibung: JCR Journal
    Schlagwort(e): Tsunami, Mediterranean, Early Warning ; 04.06. Seismology
    Repository-Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Materialart: article
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  • 6
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    Elsevier B.V.
    Publikationsdatum: 2021-02-15
    Beschreibung: This chapter presents applications of supervised learning in various geophysical disciplines, being them seismology, geodesy, magnetism, and others. For all examples, we provide a brief introduction to the geophysical background. Practical aspects, such as normalization issues and feature selection, are discussed. A posteriori considerations shed light on the geophysical problem, such as the importance of model parameters in regression, the possible nonuniqueness in inversion, and flaws in the definition of targets. We demonstrate multilayer perceptrons (MLPs) as classifiers of seismic waveforms. Besides, we show how the use of MLP is straightforward in the context of inversion of various kinds of data, for example, seismic, geodetic, and magnetic. Regression with MLP is applied to magnetotelluric and seismic data. Multiclass classification with support vector machine (SVM) is discussed for infrasound waveforms and volcanic rocks using geochemical characteristics. We introduce the use of SVM in the context of regression, which is formally less immediate than for MLP, but yields good results. An example deals with empirical ground motion estimation during earthquakes. In hidden Markov models and Bayesian networks one considers the interrelation between observations rather than single patterns. We show their benefits in various applications, from seismic waveform classification aimed at the forecast of volcanic unrest up to their use in tsunami early-warning systems.
    Beschreibung: Published
    Beschreibung: 127-187
    Beschreibung: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Schlagwort(e): pattern recognition ; supervised learning ; multilayer perceptrons ; seismic data ; magnetotelluric data ; infrasound waveforms ; volcanic rocks ; geochemical characteristics ; 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
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  • 7
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    Unbekannt
    Elsevier B.V.
    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
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  • 8
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    Unbekannt
    Elsevier B.V.
    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
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  • 9
    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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  • 10
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    Unbekannt
    Elsevier B.V.
    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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