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  • 04.06. Seismology  (7)
  • 04. Solid Earth::04.04. Geology::04.04.12. Fluid Geochemistry
  • Elsevier B.V.  (9)
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  • 1
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    Supervised learning (2021)
    Elsevier B.V.
    Details
    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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  • 2
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    Patterns, objects, and features (2021)
    Elsevier B.V.
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    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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  • 3
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    A posteriori analyses—advantages and pitfalls of pattern recognition techniques (2021)
    Elsevier B.V.
    Details
    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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  • 4
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    Unsupervised learning (2021)
    Elsevier B.V.
    Details
    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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  • 5
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    Applications with unsupervised learning (2021)
    Elsevier B.V.
    Details
    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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  • 6
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    Software manuals (2021)
    Elsevier B.V.
    Details
    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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  • 7
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    Applications of supervised learning (2021)
    Elsevier B.V.
    Details
    Publication Date: 2021-02-15
    Description: 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.
    Description: Published
    Description: 127-187
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Keywords: 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)
    Type: book chapter
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  • 8
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    Insights into fluid circulation across the Pernicana Fault (Mt. Etna, Italy) and implications for flank instability (2010)
    Elsevier B.V.
    Details
    Publication Date: 2017-04-04
    Description: We conducted geophysical–geochemical measurements on a ∼2 kmN–S profile cutting across the Pernicana Fault, one of the most active tectonic features on the NE flank of Mt. Etna. The profile passes from the unstable E flank of the volcano (to the south) to the stable N flank and significant fluctuations in electrical resistivity, self-potential, and soil gas emissions (CO2, Rn and Th) are found. The detailed multidisciplinary analysis reveals a complex interplay between the structural setting, uprising hydrothermal fluids, meteoric fluids percolating downwards, ground permeability, and surface topography. In particular, the recovered fluid circulation model highlights that the southern sector is heavily fractured and faulted, allowing the formation of convective hydrothermal cells. Although the existence of a hydrothermal system in a volcanic area does not surprise, these results have great implications in terms of flank dynamics at Mt. Etna. Indeed, the hydrothermal activity, interacting with the Pernicana Fault activity, could enhance the flank instability. Our approach should be further extended along the full extent of the boundary between the stable and unstable sectors of Etna for a better evaluation of the geohazard in this active tectonic area.
    Description: This work was partly financed by the DPC-INGV FLANK and LAVA Projects.
    Description: Published
    Description: 137–142
    Description: 1.5. TTC - Sorveglianza dell'attività eruttiva dei vulcani
    Description: 3.2. Tettonica attiva
    Description: 4.5. Studi sul degassamento naturale e sui gas petroliferi
    Description: JCR Journal
    Description: reserved
    Keywords: Pernicana Fault ; fluid circulation ; structural geology ; Etna ; magnetic ; electrical methods ; 04. Solid Earth::04.02. Exploration geophysics::04.02.99. General or miscellaneous ; 04. Solid Earth::04.02. Exploration geophysics::04.02.01. Geochemical exploration ; 04. Solid Earth::04.02. Exploration geophysics::04.02.04. Magnetic and electrical methods ; 04. Solid Earth::04.02. Exploration geophysics::04.02.05. Downhole, radioactivity, remote sensing, and other methods ; 04. Solid Earth::04.02. Exploration geophysics::04.02.07. Instruments and techniques ; 04. Solid Earth::04.04. Geology::04.04.99. General or miscellaneous ; 04. Solid Earth::04.04. Geology::04.04.06. Rheology, friction, and structure of fault zones ; 04. Solid Earth::04.04. Geology::04.04.07. Rock geochemistry ; 04. Solid Earth::04.04. Geology::04.04.09. Structural geology ; 04. Solid Earth::04.04. Geology::04.04.11. Instruments and techniques ; 04. Solid Earth::04.04. Geology::04.04.12. Fluid Geochemistry ; 04. Solid Earth::04.07. Tectonophysics::04.07.99. General or miscellaneous ; 04. Solid Earth::04.07. Tectonophysics::04.07.07. Tectonics ; 04. Solid Earth::04.08. Volcanology::04.08.99. General or miscellaneous ; 04. Solid Earth::04.08. Volcanology::04.08.01. Gases ; 04. Solid Earth::04.08. Volcanology::04.08.02. Experimental volcanism ; 04. Solid Earth::04.08. Volcanology::04.08.04. Thermodynamics ; 04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring ; 04. Solid Earth::04.08. Volcanology::04.08.07. Instruments and techniques ; 04. Solid Earth::04.08. Volcanology::04.08.08. Volcanic risk ; 05. General::05.08. Risk::05.08.99. General or miscellaneous
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 9
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    A new hydrothermal scenario for the 2006 Lusi eruption, Indonesia. Insights from gas geochemistry (2012)
    Elsevier B.V.
    Details
    Publication Date: 2017-04-04
    Description: The 29th of May 2006 gas and mud eruptions suddenly appeared along the Watukosek fault in the north east of Java, Indonesia. Within a few weeks several villages were submerged by boiling mud. The most prominent eruption site was named Lusi. To date (November 2011) Lusi is still active and a ~7 km2 area is covered by the burst mud breccia. The mechanisms responsible for this devastating eruption remain elusive. While there is consensus about the origin of the erupted mud, the source of water is uncertain, the origin of the gas is unknown and the trigger of the eruption is still debated. In order to shed light on these unknowns, we acquired a wide set of data of molecular and isotopic composition of gas sampled in several Lusi vents, in the surrounding mud volcanoes, in the closest natural gas field (Wunut), and in the hydrothermal vents at the neighbouring volcanic complex in the period 2006–2011. The boiling fluids erupted in the crater zone are apparently CO2-dominated, while colder CH4-dominated and C2–C3 bearing fluids are identified at several sites around the crater zone. Gas genetic diagrams, maturity plots and gas generation modelling suggest that the hydrocarbons are thermogenic (δ¹³C1 up to −35‰; δ¹³C2 up to −20‰), deriving from marine kerogen with maturity of at least 1.5%Ro, for instance in the ~4400 m deep Ngimbang source rocks. CO2 released from the crater and surrounding seeps is also thermogenic (δ¹³C from −15 to −24‰) related to kerogen decarboxylation or thermal CH4 oxidation in deep rocks, although three vents just outside the crater showed an apparent inorganic signature (−7.5 ‰〈 δ¹³C=−0.5‰) associated to mantle helium (R/Ra up to 6.5). High CO2–CH4 equilibrium temperatures (200–400 °C) are typical of thermally altered hydrocarbons or organic matter. The data suggest mainly thermally altered organic sources for the erupted gases, deeper sourced than the mud and water (Upper Kalibeng shales). These results are consistent with a scenario of deep seated (〉4000 m) magmatic intrusions and hydrothermal fluids responsible for the enhanced heat that altered source rocks and/or gas reservoirs. The neighbouring magmatic Arjuno complex and its fluid–pressure system combined with high seismic activity could have played a key role in the Lusi genesis and evolution. Within this new model framework, Lusi is better understood as a sediment-hosted hydrothermal system rather than a mud volcano.
    Description: Published
    Description: 305–318
    Description: 4.5. Studi sul degassamento naturale e sui gas petroliferi
    Description: JCR Journal
    Description: reserved
    Keywords: Lusi eruption ; sediment-hosted hydrothermal system ; mud volcanoes ; gas origin ; CO2 and CH4 ; mantle ; 04. Solid Earth::04.04. Geology::04.04.12. Fluid Geochemistry
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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