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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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    Retrospective short-term forecasting experiment in Italy based on the occurrence of strong (fore) shocks (2021)
    Oxford University Press
    Details
    Publication Date: 2021-03-15
    Description: In a recent work we computed the relative frequencies with which strong shocks (4.0≤Mw〈5.0), widely felt by the population were followed in the same area by potentially destructive main shocks (Mw≥5.0) in Italy. Assuming the stationarity of the seismic release properties, such frequencies can be tentatively used to estimate the probabilities of potentially destructive shocks after the occurrence of future strong shocks. This allows us to set up an alarm-based forecasting hypothesis related to strong foreshocks occurrence. Such hypothesis is tested retrospectively on the data of a homogenized seismic catalogue of the Italian area against a purely random hypothesis that simply forecasts the target main shocks proportionally to the space-time fraction occupied by the alarms. We compute the latter fraction in two ways a) as the ratio between the average time covered by the alarms in each area and the total duration of the forecasting experiment (60 years) and b) as the same ratio but weighted by the past frequency of occurrence of earthquakes in each area. In both cases the overall retrospective performance of our forecasting algorithm is definitely better than the random case. Considering an alarm duration of three months, the algorithm retrospectively forecasts more than 70% of all shocks with Mw5.5 occurred in Italy from 1960 to 2019 with a total space-time fraction covered by the alarms of the order of 2%. Considering the same space-time coverage, the algorithm is also able to retrospectively forecasts more than 40% of the first main shocks with Mw5.5 of the seismic sequences occurred in the same time interval. Given the good reliability of our results, the forecasting algorithm is set and ready to be tested also prospectively, in parallel to other ongoing procedures operating on the Italian territory.
    Description: This paper benefitted from funding provided by the European Union within the ambit of the H2020 project RISE (No. 821115), which in particular fully financed the PhD grant of one of the authors (E.B.).
    Description: Published
    Description: 1192–1206
    Description: 6T. Studi di pericolosità sismica e da maremoto
    Description: JCR Journal
    Keywords: Earthquake interaction ; Statistical seismology ; forecasting, ; prediction ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 4
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    An automatically generated high-resolution earthquake catalogue for the 2016–2017 Central Italy seismic sequence, including P and S phase arrival times (2021)
    Oxford University Press
    Details
    Publication Date: 2021-03-24
    Description: The 2016–17 central Italy earthquake sequence began with the first mainshock near the town of Amatrice on August 24 (MW 6.0), and was followed by two subsequent large events near Visso on October 26 (MW 5.9) and Norcia on October 30 (MW 6.5), plus a cluster of 4 events with MW 〉 5.0 within few hours on January 18, 2017. The affected area had been monitored before the sequence started by the permanent Italian National Seismic Network (RSNC), and was enhanced during the sequence by temporary stations deployed by the National Institute of Geophysics and Volcanology and the British Geological Survey. By the middle of September, there was a dense network of 155 stations, with a mean separation in the epicentral area of 6–10 km, comparable to the most likely earthquake depth range in the region. This network configuration was kept stable for an entire year, producing 2.5 TB of continuous waveform recordings. Here we describe how this data was used to develop a large and comprehensive earthquake catalogue using the Complete Automatic Seismic Processor (CASP) procedure. This procedure detected more than 450,000 events in the year following the first mainshock, and determined their phase arrival times through an advanced picker engine (RSNI-Picker2), producing a set of about 7 million P- and 10 million S-wave arrival times. These were then used to locate the events using a non-linear location (NLL) algorithm, a 1D velocity model calibrated for the area, and station corrections and then to compute their local magnitudes (ML). The procedure was validated by comparison of the derived data for phase picks and earthquake parameters with a handpicked reference catalogue (hereinafter referred to as ‘RefCat’). The automated procedure takes less than 12 hours on an Intel Core-i7 workstation to analyse the primary waveform data and to detect and locate 3000 events on the most seismically active day of the sequence. This proves the concept that the CASP algorithm can provide effectively real-time data for input into daily operational earthquake forecasts, The results show that there have been significant improvements compared to RefCat obtained in the same period using manual phase picks. The number of detected and located events is higher (from 84,401 to 450,000), the magnitude of completeness is lower (from ML 1.4 to 0.6), and also the number of phase picks is greater with an average number of 72 picked arrival for a ML = 1.4 compared with 30 phases for RefCat using manual phase picking. These propagate into formal uncertainties of ± 0.9km in epicentral location and ± 1.5km in depth for the enhanced catalogue for the vast majority of the events. Together, these provide a significant improvement in the resolution of fine structures such as local planar structures and clusters, in particular the identification of shallow events occurring in parts of the crust previously thought to be inactive. The lower completeness magnitude provides a rich data set for development and testing of analysis techniques of seismic sequences evolution, including real-time, operational monitoring of b-value, time-dependent hazard evaluation and aftershock forecasting.
    Description: Published
    Description: 555–571
    Description: 3T. Fisica dei terremoti e Sorgente Sismica
    Description: JCR Journal
    Keywords: 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 5
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    Erratum: On seismic ambient noise cross-correlation and surface-wave attenuation (2021)
    Oxford University Press
    Details
    Publication Date: 2021-05-12
    Description: erratum paper
    Description: Published
    Description: 1090-1092
    Description: 1T. Struttura della Terra
    Description: JCR Journal
    Keywords: Theoretical seismology ; Seismic attenuation ; Seismic noise ; Surface waves ; Free oscillations ; Seismic interferometry ; 04.06. Seismology ; 04.01. Earth Interior
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 6
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    The Deep Plumbing System of Ischia: a Physico-chemical Window on the Fluid-saturated and CO2-sustained Neapolitan Volcanism (Southern Italy) (2021)
    Oxford University Press
    Details
    Publication Date: 2021-05-12
    Description: Ischia, a volcanic island located 18 miles SW of Naples (Southern Italy), is a densely populated active caldera that last erupted in AD 1302. Melt inclusions in phenocrysts of the Vateliero and Cava Nocelle shoshonite^latite eruptive products (6th to 4th centuries BC) constrain the structure and nature of the Ischia deep magmatic feeding system.Their geochemical characteristics make Ischia a natural borehole for probing the physico-chemical conditions of magma generation in mantle contaminated by slab-derived fluids or melts, largely dominated by CO2.Volatile concentrations in olivine-hosted melt inclusions require gas^melt equilibria at between 3 and 18 km depth. In agreement with what has already been demonstrated at the other neighboring Neapolitan volcanoes (Procida, Campi Flegrei caldera and Somma^Vesuvius volcanic complex), a major crystallization depth at 8^10 km has been identified.The analyzed melt inclusions provide clear evidence for CO2-dominated gas fluxing and consequent dehydration of magma batches stagnating at crustal discontinuities. Gas fluxing is further supported by selective enrichment in K owing to fluid-transfer during magma differentiation.This takes place under oxidized conditions (Fe3þ /Fe 0·3) that can be fixed by an equimolar proportion of divalent and trivalent iron in the melt if post-entrapment crystallization of the host olivine is discarded.The melt inclusion data, together with data from the literature for other Neapolitan volcanoes, show that magmatism and volcanism in the Neapolitan area, despite differences in composition and eruption dynamics, are closely linked to supercritical CO2-rich fluids. These fluids are produced by devolatilization of subducting terrigenous^pelagic metasediments and infiltrate the overlying mantle wedge, generate magmas and control their ascent up to eruption. Geochemical characteristics of Ischia and the other Neapolitan volcanoes reveal that the extent of fluid or melt contamination of the pre-subduction asthenospheric mantle wedge was similar among these volcanoes. However, differences in the isotopic compositions of the erupted magmas (more enriched in radiogenic Sr at Ischia, Campi Flegrei and Somma^Vesuvius with respect to Procida) and the amount of H2O in the plumbing system of these volcanoes (almost double at Ischia, Campi Flegrei and Somma^Vesuvius than at Procida) reflect the different flow-rates of deep slab-derived fluids or melts through the mantle wedge, which, in turn, control the amount of generated magma.The high bulk permeability of the lithosphere below Ischia, Campi Flegrei and Somma^Vesuvius, determined by the occurrence of intersecting NW^SE and NE^SW regional fault systems, favours fluid ascent and accumulation at crustal levels, with consequent larger magma production and storage than at Procida, located along the NE^SW system.
    Description: Published
    Description: 951-984
    Description: 2V. Struttura e sistema di alimentazione dei vulcani
    Description: 3V. Proprietà chimico-fisiche dei magmi e dei prodotti vulcanici
    Description: 4V. Processi pre-eruttivi
    Description: 6A. Geochimica per l'ambiente e geologia medica
    Description: JCR Journal
    Keywords: CO2-fluxing ; melt inclusions ; redox state ; trachybasalts
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 7
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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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  • 8
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    Unsupervised learning (2021)
    Elsevier B.V.
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    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
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    Applications with unsupervised learning (2021)
    Elsevier B.V.
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    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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    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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  • 11
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    On the Heterogeneity of the Earthquake rupture (2021)
    Oxford University Press
    Details
    Publication Date: 2021-04-14
    Description: The scaling of earthquake parameters with seismic moment and its interpretation in terms of self- similarity is still debated in the literature. We address this question by examining a worldwide compilation of corner frequency-based and elastic rebound theory (ERT)-based fault slip, area and stress drop values for earthquakes ranging in magnitude from -0.7 to 7.8. We find that corner frequency estimates of slip (and stress drop) scale differently than those inferred from the ERT approach, where the latter deviates from the generally accepted constant stress drop behavior of so- called self-similar scaling models. We also find that average slips from finite-source models are consistent with corner frequency scaling, whereas peak slip values are more consistent with the ERT scaling. The different scaling of corner frequency- and ERT-based estimates of slip and stress drop with earthquake size is interpreted in terms of heterogeneity of the rupture process. ERT-based estimates of stress drop decrease with seismic moment suggesting a self-affine behavior. Despite the inferred heterogeneity at all scales, we do not observe a clear effect on the Brune stress drop scaling with earthquake size.
    Description: Published
    Description: 1771–1781
    Description: 2T. Deformazione crostale attiva
    Description: JCR Journal
    Keywords: Earthquake dynamics ; Earthquake source observations ; Dynamics and mechanics of faulting ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 12
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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)
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  • 13
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    Crustal and uppermost mantle shear wave velocity structure beneath the Middle East from surface wave tomography (2021)
    Oxford University Press
    Details
    Publication Date: 2021-05-12
    Description: We have constructed a 3-D shear wave velocity (Vs) model for the crust and uppermost mantle beneath the Middle East using Rayleigh wave records obtained from ambient-noise cross-correlations and regional earthquakes. We combined one decade of data collected from 852 permanent and temporary broad-band stations in the region to calculate group-velocity dispersion curves. A compilation of 〉54 000 ray paths provides reliable group-velocity measurements for periods between 2 and 150 s. Path-averaged group velocities calculated at different periods were inverted for 2-D group-velocity maps. To overcome the problem of heterogeneous ray coverage, we used an adaptive grid parametrization for the group-velocity tomographic inversion. We then sample the period-dependent group-velocity field at each cell of a predefined grid to generate 1-D group-velocity dispersion curves, which are subsequently inverted for 1-D Vs models beneath each cell and combined to approximate the 3-D Vs structure of the area. The Vs model shows low velocities at shallow depths (5–10 km) beneath the Mesopotamian foredeep, South Caspian Basin, eastern Mediterranean and the Black Sea, in coincidence with deep sedimentary basins. Shallow high-velocity anomalies are observed in regions such as the Arabian Shield, Anatolian Plateau and Central Iran, which are dominated by widespread magmatic exposures. In the 10–20 km depth range, we find evidence for a band of high velocities (〉4.0 km s–1) along the southern Red Sea and Arabian Shield, indicating the presence of upper mantle rocks. Our 3-D velocity model exhibits high velocities in the depth range of 30–50 km beneath western Arabia, eastern Mediterranean, Central Iranian Block, South Caspian Basin and the Black Sea, possibly indicating a relatively thin crust. In contrast, the Zagros mountain range, the Sanandaj-Sirjan metamorphic zone in western central Iran, the easternmost Anatolian plateau and Lesser Caucasus are characterized by low velocities at these depths. Some of these anomalies may be related to thick crustal roots that support the high topography of these regions. In the upper mantle depth range, high-velocity anomalies are obtained beneath the Arabian Platform, southern Zagros, Persian Gulf and the eastern Mediterranean, in contrast to low velocities beneath the Red Sea, Arabian Shield, Afar depression, eastern Turkey and Lut Block in eastern Iran. Our Vs model may be used as a new reference crustal model for the Middle East in a broad range of future studies.
    Description: Published
    Description: 1349-1365
    Description: 1T. Struttura della Terra
    Description: JCR Journal
    Keywords: 04.01. Earth Interior ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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