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  • 04.06. Seismology
  • Creep observations and analysis
  • Springer  (21)
  • Elsevier B.V.  (7)
  • Wiley-AGU  (7)
  • EGU - Copernicus
  • Wiley
  • 1
    Publication Date: 2020-11-12
    Description: Public concern about anthropogenic seismic- ity in Italy first arose in the aftermath of the deadly M ≈ 6 earthquakes that hit the Emilia-Romagna region (northern Italy) in May 2012. As these events occurred in a (tectonically active) region of oil and gas production and storage, the question was raised, whether stress perturbations due to underground industrial activities could have induced or triggered the shocks. Following expert recommendations, in 2014, the Italian Oil & Gas Safety Authority (DGS-UNMIG, Ministry of Economic Development) published guidelines (ILG - Indirizzi e linee guida per il monitoraggio della sismicità, delle deformazioni del suolo e delle pressioni di poro nell’ambito delle attività antropiche), describing regula- tions regarding hydrocarbon extraction, waste-water in- jection and gas storage that could also be adapted to other technologies, such as dams, geothermal systems, CO2 storage, and mining. The ILG describe the frame- work for the different actors involved in monitoring activities, their relationship and responsibilities, the procedure to be followed in case of variations of mon- itored parameters, the need for in-depth scientific anal- yses, the definition of different alert levels, their mean- ing and the parameters to be used to activate such alerts. Four alert levels are defined, the transition among which follows a decision to be taken jointly by relevant au- thorities and industrial operator on the basis of evalua- tion of several monitored parameters (micro-seismicity, ground deformation, pore pressure) carried on by a scientific-technical agency. Only in the case of liquid reinjection, the alert levels are automatically activated on the basis of exceedance of thresholds for earthquake magnitude and ground shaking – in what is generally known as a Traffic Light System (TLS). Istituto Nazionale di Geofisica e Vulcanologia has been charged by the Italian oil and gas safety authority (DGS- UNMIG) to apply the ILG in three test cases (two oil extraction and one gas storage plants). The ILG indeed represent a very important and positive innovation, as they constitute official guidelines to coherently regulate monitoring activity on a national scale. While pilot studies are still mostly under way, we may point out merits of the whole framework, and a few possible critical issues, requiring special care in the implementa- tion. Attention areas of adjacent reservoirs, possibly licenced to different operators, may overlap, hence mak- ing the point for joint monitoring, also in view of the possible interaction between stress changes related to the different reservoirs. The prescribed initial blank- level monitoring stage, aimed at assessing background seismicity, may lose significance in case of nearby ac- tive production. Magnitude – a critical parameter used to define a possible step-up in activation levels – has inherent uncertainty and can be evaluated using differ- ent scales. A final comment considers the fact that relevance of TLS, most frequently used in hydraulic fracturing operations, may not be high in case of trig- gered tectonic events.
    Description: Published
    Description: 1015–1028
    Description: 1IT. Reti di monitoraggio e sorveglianza
    Description: JCR Journal
    Keywords: Anthropogenic seismicity ; Alert system ; Monitoring guidelines ; 04.06. Seismology ; 05.04. Instrumentation and techniques of general interest
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
    Publication Date: 2021-01-22
    Description: 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.
    Description: Published
    Description: 67–74
    Description: 1SR TERREMOTI - Sorveglianza Sismica e Allerta Tsunami
    Description: N/A or not JCR
    Keywords: Vesuvius ; Spectral Analisys ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
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    Elsevier B.V.
    Publication Date: 2021-02-01
    Description: 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.
    Description: Published
    Description: 33-85
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: 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)
    Type: book chapter
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  • 4
    Publication Date: 2021-02-01
    Description: 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.
    Description: Published
    Description: 3-13
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: 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)
    Type: book chapter
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  • 5
    Publication Date: 2020-10-01
    Description: Several months of ambient seismic noise recordings are used for investigating the distribution of elastic properties in the Fucino Plain, one of the largest intermontane tectonic depressions of the Italian Apennine chain (Central Italy). The Plain is characterized by a low level of seismicity but the presence of several active faults makes it an Italian area of high seismic hazard. The most recent and strongest seismic event in Fucino Plain occurred in the 1915 (Avezzano earthquake) and it represents one of the most energetic events (Ms = 7.0) happened in central Apennines. Inter-stations Green’s functions are reconstructed by the cross-correlation of continuous ambient noise data recorded from twelve seismic velocimeters deployed around the Avezzano city, and organized in two different temporally sub-networks. The aim of cross-correlation analysis is to extract surface waves from Green’s functions for investigating the dispersive response of the structure. We analyzed the temporal stability of the cross-correlated signals that is used as an indicator of reliability of measurements and as criteria to select the Green’s functions to analyze
    Description: Published
    Description: 1173-1176
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: Cross correlation ; Noise ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book chapter
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  • 6
    Publication Date: 2020-11-30
    Description: Buildings close to each other can perform different behaviour despite its similar seismic vulnerability. This effect is mainly due to the local seismic response connected to the characteristics of the shallow soil layers, especially when we move away from the epicentral area and the near field motion reduces its importance among the total amount of shaking. In this paper we show some results of the microzonation project of the Avezzano municipality, a town located in the southwestern portion of the Abruzzi region, which experienced the severe effects of the January 13th, 1915 M 7.0 earthquake. Starting from a particularly detailed knowledge of the geological characteristics of outcropping lithologies and inferring the trend of subsoil geometries, we explored the role played by the near-surface geology in causing variability of the ground motion by analysing a large database of earthquakes and microtremor recordings acquired by temporary seismological networks using classical site-reference and non-reference spectral techniques. Based on the obtained results we can seismically characterize all the municipal territory not only in terms of fundamental resonance frequency, useful in drawing maps of seismic microzonation and design geological sections, but also of amplification factors helpful in verifying numerical modelling of seismic response as required by national microzonation guidelines. We have also found many criticisms that need a more detailed analysis in order to establish the cause of these anomalies.
    Description: Published
    Description: 1153-1157
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: Microzonation ; Site response ; Spectral techniques ; Seismic amplification ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book chapter
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  • 7
    Publication Date: 2021-02-01
    Description: 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.
    Description: Published
    Description: 237-259
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: 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)
    Type: book chapter
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  • 8
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    Elsevier B.V.
    Publication Date: 2021-02-01
    Description: 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.
    Description: Published
    Description: 87-124
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: 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)
    Type: book chapter
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  • 9
    Publication Date: 2021-02-01
    Description: 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.
    Description: Published
    Description: 189-234
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: 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)
    Type: book chapter
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  • 10
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    Elsevier B.V.
    Publication Date: 2021-02-01
    Description: 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.”
    Description: Published
    Description: 261-313
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: 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)
    Type: book chapter
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