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  • 1
    Electronic Resource
    Electronic Resource
    An adaptive-grid formalism for traveltime tomography (1995)
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical journal international 121 (1995), S. 0 
    Details
    ISSN: 1365-246X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences
    Notes: The use of an adaptive-grid formalism for seismic, non-linear traveltime tomography is proposed. The method is based on a parametric representation of the velocity model and involves the simultaneous inversion for both velocity and position of the grid points of the model discretization mesh. Therefore, the method seeks the optimal grid configuration to define the model. Cubic B-spline basis functions have been used for model representation as they are particularly versatile in the reproduction of geologically complex structures. The traveltimes are calculated using a new initial-value ray tracer that calculates the ray trajectories directly in the parametric domain.The method is tested against synthetic data generated for various cross-hole geometries. It is found that, in parts of the model having good ray coverage, the method accurately retrieves velocity anomalies of arbitrary shape using a generally small number of grid points of the inversion discretization mesh. With the exception of initial meshes that are too coarse to describe accurately the complexity of the true structure, the method retrieves nearly identical final models regardless of the predefined node spacing and node configuration. Therefore, the method can avoid very fine discretization, and matrix sparseness, one of the main sources of indeterminacy, is similarly avoided. When compared with standard methods entailing a similar total number of inversion parameters, our results show the pitfalls that may derive from the a priori assigning of a fixed-grid mesh. Overall, the parametric representation leads to some saving in the total number of inversion parameters when the structure consists of sparse distributions of irregular features. Other styles of model may be better recovered by regular grids for a given number of inversion parameters.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-246X.1995.tb05728.x
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Testing the reliability of Vp/Vs anomalies in traveltime tomography (1993)
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical journal international 114 (1993), S. 0 
    Details
    ISSN: 1365-246X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences
    Notes: In traveltime tomography, Vp/Vs models calculated as ratio between the resolved P and S models are affected by the different ray coverages and phase-onset reading accuracies of P and S waves. the result of this is that the computed Vp/Vs models can display large fluctuations which are unrelated to the true structure. Introduction of some P-S coupling (e.g. proportionality, correlation) as a priori data permits us to stabilize the resulting Vp/Vs model about a preselected average value and to minimize the insurgence of fictitious Vp/Vs anomalies. In general, addition of P-S coupling trades off with data misfit and adoption of this technique is suggested for robustness tests of resolved Vp/Vs features. the technique is applied to a synthetic data set generated using an idealized fault structure and the source-receiver geometry existing at Parkfield, California.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-246X.1993.tb03928.x
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    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Velocity model inversion using parametric curves (1993)
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical journal international 115 (1993), S. 0 
    Details
    ISSN: 1365-246X
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Geosciences
    Notes: The use of parametric curves to define velocity models in seismic traveltime tomography is proposed. This approach provides increased flexibility in shaping the velocity models because it allows inversion for both velocity and grid point adjustments. The method is applied to a data set generated from a 1-D profile having sharp discontinuities. The results are compared with those obtained from other methods currently in use for traveltime tomography. It is found that large gradients and discontinuities can be retrieved with improved accuracy and that instabilities caused by non-optimal prior selection of the inversion grid can be avoided. The results appear to be robust when the sizes of both data and model space are reduced which makes the method appealing for the solution of large-scale tomographic problems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1365-246X.1993.tb01190.x
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    Paper (German National Licenses)
    Fulltext
  • 4
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    Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers (2021)
    arXiv
    In:  (Submitted) arXiv e-prints .
    Details
    Publication Date: 2021-11-24
    Description: Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches and even achieve human-like performance under certain circumstances. However, as most studies differ in the datasets and exact evaluation tasks studied, it is yet unclear how the different approaches compare to each other. Furthermore, there are no systematic studies how the models perform in a cross-domain scenario, i.e., when applied to data with different characteristics. Here, we address these questions by conducting a large-scale benchmark study. We compare six previously published deep learning models on eight datasets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer-Kradolfer picker. Overall, we observe the best performance for EQTransformer, GPD and PhaseNet, with EQTransformer having a small advantage for teleseismic data. Furthermore, we conduct a cross-domain study, in which we analyze model performance on datasets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but not from regional to teleseismic data or vice versa. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily compare new models or evaluate performance on new datasets, beyond those presented here. Furthermore, we make all trained models available through the SeisBench framework, giving end-users an easy way to apply these models in seismological analysis.
    Type: Article , NonPeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - without review
  • 5
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    SeisBench - A Toolbox for Machine Learning in Seismology (2021)
    arXiv
    In:  (Submitted) arXiv e-prints .
    Details
    Publication Date: 2021-11-23
    Description: Machine Learning (ML) methods have seen widespread adoption in seismology in recent years. The abilityof these techniques to efficiently infer the statistical properties of large datasets often provides significantimprovements over traditional techniques when the number of data are large(»millions of examples). Withthe entire spectrum of seismological tasks, e.g., seismic picking and detection, magnitude and source propertyestimation, ground motion prediction, hypocentre determination; among others, now incorporating ML ap-proaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluatethese algorithms, quality controlled benchmark datasets that contain representative class distributions are vital.In addition to this, models require implementation through a common framework to facilitate comparison.Accessing these various benchmark datasets for training and implementing the standardization of models iscurrently a time-consuming process, hindering further advancement of ML techniques within seismology. Thesedevelopment bottlenecks also affect ’practitioners’ seeking to deploy the latest models on seismic data, withouthaving to necessarily learn entirely new ML frameworks to perform this task. We present SeisBench as a soft-ware package to tackle these issues. SeisBench is an open-source framework for deploying ML in seismology.SeisBench standardises access to both models and datasets, whilst also providing a range of common processingand data augmentation operations through the API. Through SeisBench, users can access several seismologicalML models and benchmark datasets available in the literature via a single interface. SeisBench is built to beextensible, with community involvement encouraged to expand the package. Having such frameworks availablefor accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the nextgeneration of ML techniques to seismic data.
    Type: Article , NonPeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - without review
  • 6
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    Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers (2022)
    AGU (American Geophysical Union) | Wiley
    Details
    Publication Date: 2024-02-07
    Description: Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches and even achieve human-like performance under certain circumstances. However, as most studies differ in the datasets and exact evaluation tasks studied, it is yet unclear how the different approaches compare to each other. Furthermore, there are no systematic studies how the models perform in a cross-domain scenario, i.e., when applied to data with different characteristics. Here, we address these questions by conducting a large-scale benchmark study. We compare six previously published deep learning models on eight datasets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer-Kradolfer picker. Overall, we observe the best performance for EQTransformer, GPD and PhaseNet, with EQTransformer having a small advantage for teleseismic data. Furthermore, we conduct a cross-domain study, in which we analyze model performance on datasets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but not from regional to teleseismic data or vice versa. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily compare new models or evaluate performance on new datasets, beyond those presented here. Furthermore, we make all trained models available through the SeisBench framework, giving end-users an easy way to apply these models in seismological analysis.
    Type: Article , PeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 7
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    SeisBench - A Toolbox for Machine Learning in Seismology (2022)
    SSA (Seismological Society of America)
    Details
    Publication Date: 2024-02-07
    Description: Machine‐learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques when the number of data are large (millions of examples). With the entire spectrum of seismological tasks, for example, seismic picking and detection, magnitude and source property estimation, ground‐motion prediction, hypocenter determination, among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluate these algorithms, quality‐controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing these various benchmark datasets for training and implementing the standardization of models is currently a time‐consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect “practitioners” seeking to deploy the latest models on seismic data, without having to necessarily learn entirely new ML frameworks to perform this task. We present SeisBench as a software package to tackle these issues. SeisBench is an open‐source framework for deploying ML in seismology—available via GitHub. SeisBench standardizes access to both models and datasets, while also providing a range of common processing and data augmentation operations through the API. Through SeisBench, users can access several seismological ML models and benchmark datasets available in the literature via a single interface. SeisBench is built to be extensible, with community involvement encouraged to expand the package. Having such frameworks available for accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the next generation of ML techniques to seismic data.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 8
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    The Central Italy Seismic Sequence between August and December 2016: Analysis of Strong‐Motion Observations (2018)
    Details
    Publication Date: 2021-02-26
    Description: Since August 2016, central Italy has been struck by one of the most important seismic sequences ever recorded in the country. In this study, a strong-motion data set, consisting of nearly 10,000 waveforms, has been analyzed to gather insights about the main features of ground motion, in terms of regional vari- ability, shaking intensity, and near-source effects. In particular, the shake maps from the three main events in the sequence have been calculated to evaluate the distribution of shaking at a regional scale, and a residual analysis has been performed, aimed at interpreting the strong-motion parameters as func- tions of source distance, azimuth, and local site conditions. Par- ticular attention has been dedicated to near-source effects (i.e., hanging wall/footwall, forward-directivity, or fling-step ef- fects). Finally, ground-motion intensities in the near-source area have been discussed with respect to the values used for structural design. In general, the areas of maximum shaking appear to reflect, primarily, rupture complexity on the finite faults. Large ground-motion variability is observed along the Apennine direction (northwest–southeast) that can be attributed to source-directivity effects, especially evident in the case of small-magnitude aftershocks. Amplifications are observed in correspondence to intramountain basins, fluvial valleys, and the loose deposits along the Adriatic coast. Near-source ground motions exhibit hanging-wall effects, forward-directivity pulses, and permanent displacement.
    Description: Published
    Description: 1219-1231
    Description: 4T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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    PAPER (OA)
  • 9
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    Seismic Surveillance and Earthquake Monitoring in Italy (2021)
    SSA
    Details
    Publication Date: 2021-05-12
    Description: The Istituto Nazionale di Geofisica e Vulcanologia (INGV) is an Italian research institution with focus on earth sciences. Moreover, the INGV is the operational center for seismic surveillance and earthquake monitoring in Italy and is a part of the civil protection system as a center of expertise on seismic, volcanic, and tsunami risks.INGV operates the Italian National Seismic Network and other networks at national scale and is a primary node of the European Integrated Data Archive for archiving and distributing strong‐motion and weak‐motion seismic recordings. In the control room in Rome, INGV staff performs seismic surveillance and tsunami warning services; in Catania and Naples, the control rooms are devoted to volcanic surveillance. Volcano monitoring includes locating earthquakes in the regions around the Sicilian (Etna, Eolian Islands, and Pantelleria) and the Campanian (Vesuvius, Campi Fregrei, and Ischia) active volcanoes. The tsunami warning is based on earthquake location and magnitude (M) evaluation for moderate to large events in the Mediterranean region and also around the world. The technologists of the institute tuned the data acquisition system to accomplish, in near real time, automatic earthquake detection, hypocenter and magnitude determination, and evaluation of several seismological products (e.g., moment tensors and ShakeMaps). Database archiving of all parametric results is closely linked to the existing procedures of the INGV seismic surveillance environment and surveillance procedures. Earthquake information is routinely revised by the analysts of the Italian seismic bulletin. INGV provides earthquake information to the Department of Civil Protection (Dipartimento di Protezione Civile) to the scientific community and to the public through the web and social media. We aim at illustrating different aspects of earthquake monitoring at INGV: (1) network operations; (2) organizational structure and the hardware and software used; and (3) communication, including recent developments and planned improvements.
    Description: FISR SOIR DPC
    Description: Published
    Description: 1659–1671
    Description: 1SR TERREMOTI - Sorveglianza Sismica e Allerta Tsunami
    Description: JCR Journal
    Keywords: Seismic surveillance ; earthquake location and magnitude
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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    PAPER (OA)
  • 10
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    EPOS Seismology services and their users (2018)
    European Geosciences Union (EGU)
    Details
    Publication Date: 2020-12-15
    Description: The construction of seismological community services for the European Plate Observing System Research Infrastructure (EPOS) is by now well under way. A significant number of services are already operational, largely based on those existing at established institutions or collaborations like ORFEUS, EMSC, AHEAD and EFEHR, and more are being added to be ready for internal validation by late 2017. In this presentation we focus on a number of issues related to the interaction of the community of users with the services provided by the seismological part of the EPOS research infrastructure. How users interact with a service (and how satisfied they are with this interaction) is viewed as one important component of the validation of a service within EPOS, and certainly is key to the uptake of a service and from that also it’s attributed value. Within EPOS Seismology, the following aspects of user interaction have already surfaced: a) User identification (and potential tracking) versus ease-of-access and openness Requesting users to identify themselves when accessing a service provides various advantages to providers and users (e.g. quantifying & qualifying the service use, customization of services and interfaces, handling access rights and quotas), but may impact the ease of access and also shy away users who don’t wish to be identified for whatever reason. b) Service availability versus cost There is a clear and prominent connection between the availability of a service, both regarding uptime and capacity, and its operational cost (IT systems and personnel), and it is often not clear where to draw the line (and based on which considerations). In connection to that, how to best utilize third-party IT infrastructures (either commercial or public), and what the long-term cost implications of that might be, is equally open. c) Licensing and attribution The issue of intellectual property and associated licensing policies for data, products and services is only recently gaining more attention in the community. Whether at all, and if yes then how to license, is still diversely discussed, while on national level more and more legislative requirements create boundary conditions that need to be respected. Attribution (of service use and of data/product origin) is only one related aspect, but of high importance the scientific world. In EPOS Seismology we attempt to find common approaches to address the above issues, also closely co-ordinated to the developments across the other EPOS domains. In this presentation we discuss the current strategies, potential solutions identified, and remaining open questions.
    Description: H2020 Project EPOS-IP, Cordis Project ID 676564
    Description: Published
    Description: Vienna, Austria
    Description: 4T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: 4IT. Banche dati
    Keywords: seismology ; data dissemination ; 04. Solid Earth ; 04.06. Seismology ; 05.02. Data dissemination
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Abstract
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