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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
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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
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 3
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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
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 4
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    Coordinated and Interoperable Seismological Data and Product Services in Europe: the EPOS Thematic Core Service for Seismology (2023)
    INGV
    Details
    Publication Date: 2023-01-18
    Description: In this article we describe EPOS Seismology, the Thematic Core Service consortium for the seismology domain within the European Plate Observing System infrastructure. EPOS Seismology was developed alongside the build-up of EPOS during the last decade, in close collaboration between the existing pan-European seismological initiatives ORFEUS (Observatories and Research Facilities for European Seismology), EMSC (Euro-Mediterranean Seismological Center) and EFEHR (European Facilities for Earthquake Hazard and Risk) and their respective communities. It provides on one hand a governance framework that allows a well-coordinated interaction of the seismological community services with EPOS and its bodies, and on the other hand it strengthens the coordination among the already existing seismological initiatives with regard to data, products and service provisioning and further development. Within the EPOS Delivery Framework, ORFEUS, EMSC and EFEHR provide a wide range of services that allow open access to a vast amount of seismological data and products, following and implementing the FAIR principles and supporting open science. Services include access to raw seismic waveforms of thousands of stations together with relevant station and data quality information, parametric earthquake information of recent and historical earthquakes together with advanced event-specific products like moment tensors or source models and further ancillary services, and comprehensive seismic hazard and risk information, covering latest European scale models and their underlying data. The services continue to be available on the well-established domain-specific platforms and websites, and are also consecutively integrated with the interoperable central EPOS data infrastructure. EPOS Seismology and its participating organizations provide a consistent framework for the future development of these services and their operation as EPOS services, closely coordinated also with other international seismological initiatives, and is well set to represent the European seismological research infrastructures and their stakeholders within EPOS.
    Description: Published
    Description: DM213
    Description: 3T. Fisica dei terremoti e Sorgente Sismica
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: 6T. Studi di pericolosità sismica e da maremoto
    Description: 8T. Sismologia in tempo reale e Early Warning Sismico e da Tsunami
    Description: 4IT. Banche dati
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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    PAPER (OA)
  • 5
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    SeisBench—A Toolbox for Machine Learning in Seismology (2023)
    Seismological Society of America
    Details
    Publication Date: 2023-02-21
    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.
    Description: Published
    Description: 1695–1709
    Description: 3T. Fisica dei terremoti e Sorgente Sismica
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 6
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    Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence (2023)
    Elsevier
    Details
    Publication Date: 2023-03-20
    Description: The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project.
    Description: Published
    Description: 414-429
    Description: 6T. Studi di pericolosità sismica e da maremoto
    Description: 8T. Sismologia in tempo reale e Early Warning Sismico e da Tsunami
    Description: 4V. Processi pre-eruttivi
    Description: 6V. Pericolosità vulcanica e contributi alla stima del rischio
    Description: 3IT. Calcolo scientifico
    Description: JCR Journal
    Keywords: High performance computing ; Distributed computing ; Parallel programming ; HPC-DA-AI convergence ; Workflow development ; Workflow orchestration
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 7
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    Graph neural networks for multivariate time series regression with application to seismic data (2023)
    Springer
    Details
    Publication Date: 2023-08-29
    Description: Machine learning, with its advances in deep learning has shown great potential in analyzing time series. In many scenarios, however, additional information that can potentially improve the predictions is available. This is crucial for data that arise from e. g., sensor networks that contain information about sensor locations. Then, such spatial information can be exploited by modeling it via graph structures, along with the sequential (time series) information. Recent advances in adapting deep learning to graphs have shown potential in various tasks. However, these methods have not been adapted for time series tasks to a great extent. Most attempts have essentially consolidated around time series forecasting with small sequence lengths. Generally, these architectures are not well suited for regression or classification tasks where the value to be predicted is not strictly depending on the most recent values, but rather on the whole length of the time series. We propose TISER-GCN, a novel graph neural network architecture for processing, in particular, these long time series in a multivariate regression task. Our proposed model is tested on two seismic datasets containing earthquake waveforms, where the goal is to predict maximum intensity measurements of ground shaking at each seismic station. Our findings demonstrate promising results of our approach—with an average MSE reduction of 16.3%—compared to the best performing baselines. In addition, our approach matches the baseline scores by needing only half the input size. The results are discussed in depth with an additional ablation study.
    Description: Interreg North-West Europe program (Interreg NWE), project Di-Plast - Digital Circular Economy for the Plastics Industry (NWE729). INGV Pianeta Dinamico 2021 Tema 8 SOME (CUP D53J1900017001) funded by Italian Ministry of University and Research “Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese, legge 145/2018.
    Description: Published
    Description: 317–332
    Description: 8T. Sismologia in tempo reale e Early Warning Sismico e da Tsunami
    Description: JCR Journal
    Keywords: Graph neural networks ; Time series ; Sensors ; Convolutional neural networks ; Regression ; Earthquake ground motion ; Seismic network ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 8
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    The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data (2023)
    MDPI
    Details
    Publication Date: 2023-01-27
    Description: Non-stationary signals are often analyzed using raw waveform data or spectrograms of those data; however, the possibility of alternative time–frequency representations being more informative than the original data or spectrograms is yet to be investigated. This paper tested whether alternative time–frequency representations could be more informative for machine learning classification of seismological data. The mentioned hypothesis was evaluated by training three well-established convolutional neural networks using nine time–frequency representations. The results were compared to the base model, which was trained on the raw waveform data. The signals that were used in the experiment are three-component seismogram instances from the Local Earthquakes and Noise DataBase (LEN-DB). The results demonstrate that Pseudo Wigner–Ville and Wigner–Ville time–frequency representations yield significantly better results than the base model, while spectrogram and Margenau–Hill perform significantly worse (p 〈 0.01). Interestingly, the spectrogram, which is often used in signal analysis, had inferior performance when compared to the base model. The findings presented in this research could have notable impacts in the fields of geophysics and seismology as the phenomena that were previously hidden in the seismic noise are now more easily identified. Furthermore, the results indicate that applying Pseudo Wigner–Ville or Wigner–Ville time–frequency representations could result in a large increase in earthquakes in the catalogs and lessen the need to add new stations with an overall reduction in the costs. Finally, the proposed approach of extracting valuable information through time–frequency representations could be applied in other domains as well, such as electroencephalogram and electrocardiogram signal analysis, speech recognition, gravitational waves investigation, and so on.
    Description: COST project G2Net CA17137 A network for Gravitational Waves, Geophysics and Machine Learning.
    Description: Published
    Description: 965
    Description: 8T. Sismologia in tempo reale e Early Warning Sismico e da Tsunami
    Description: JCR Journal
    Keywords: earthquake detection; convolutional neural network; non-stationary signal analysis; classification; time–frequency representation ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 9
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    Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features? (2023)
    Details
    Publication Date: 2023-01-27
    Description: In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model, and one possible solution to this problem is transfer learning. This study aims to assess how transferable are the features between different domains of time series data and under which conditions. The effects of transfer learning are observed in terms of predictive performance of the models and their convergence rate during training. In our experiment, we use reduced data sets of 1,500 and 9,000 data instances to mimic real world conditions. Using the same scaled-down datasets, we trained two sets of machine learning models: those that were trained with transfer learning and those that were trained from scratch. Four machine learning models were used for the experiment. Transfer of knowledge was performed within the same domain of application (seismology), as well as between mutually different domains of application (seismology, speech, medicine, finance). We observe the predictive performance of the models and the convergence rate during the training. In order to confirm the validity of the obtained results, we repeated the experiments seven times and applied statistical tests to confirm the significance of the results. The general conclusion of our study is that transfer learning is very likely to either increase or not negatively affect the predictive performance of the model or its convergence rate. The collected data is analysed in more details to determine which source and target domains are compatible for transfer of knowledge. We also analyse the effect of target dataset size and the selection of model and its hyperparameters on the effects of transfer learning.
    Description: Croatian Science Foundation projects UIP-2019-04-7999, DOK-2020-01-4659 and IP-2020-02-3770, COST project G2Net CA17137 A network for Gravitational Waves, Geophysics and Machine Learning. Partially supported by the project INGV Pianeta Dinamico 2021 Tema 8 SOME (CUP D53J1900017001) funded by Italian Ministry of University and Research “Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato
    Description: Published
    Description: 107976
    Description: 8T. Sismologia in tempo reale e Early Warning Sismico e da Tsunami
    Description: JCR Journal
    Keywords: machine learning, transfer learning, time series, fine-tuning, convolutional neural networks
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 10
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    New reversible relationships between ground motion parameters and macroseismic intensity for Italy and their application in ShakeMap (2023)
    Oxford University Press - The Royal Astronomical Society
    Details
    Publication Date: 2023-01-27
    Description: We derived new, reversible relationships between macroseismic intensity (I), expressed in either the European Macroseismic (EMS-98) or the Mercalli–Cancani–Sieberg (MCS) scales and peak ground acceleration (PGA), peak ground velocity (PGV) and the spectral acceleration (SA) at 0.3, 1.0 and 3.0 s [SA(0.3), SA(1.0) and SA(3.0)] for Italy. We adopted the orthogonal distance regression technique to fit a quadratic function. This research aims to improve ground motion and intensity estimates for earthquake hazard applications, and for the calculation of shakemaps in Italy. To this end, the recently published INGe data set was used (https: //doi.org/10.13127/inge.2). The new relations are: I = 3.01 ± 0.12 + 0.86 ± 0.04 log2 PGA,σ= 0.30,σPGA = 0.25,σI = 0.16 I = 4.31 ± 0.15 + 1.99 ± 0.18 log PGV + 0.58 ± 0.18 log2 PGV,σ= 0.34,σPGV = 0.31,σI = 0.15 I = 2.77 ± 0.15 + 0.68 ± 0.03 log2 SA(0.3),σ= 0.31,σSA(0.3) = 0.28,σI = 0.14 I = 3.00 ± 0.28 + 0.91 ± 0.55 log SA(1.0) + 0.51 ± 0.20 log2 SA(1.0),σ= 0.40,σSA(1.0) = 0.38,σI = 0.14 I = 4.04 ± 0.20 + 1.63 ± 0.19 log SA(3.0) + 0.66 ± 0.20 log2 SA(3.0),σ= 0.38,σSA(3.0) = 0.35,σI = 0.14 where PGA and SAs are expressed in cm s−2 and PGV is expressed in cm s−1. Tests performed to assess the robustness and the accuracy of the results demonstrate that adoption of quadratic relationships for this regression problem is a suitable choice within the range of values of the available data set. Comparison with similar published regressions for Italy evidences that the proposed relations provide statistically significant improved fits to the data. The new relations are also tested by inserting them in the ShakeMap system of the Italian configuration evidencing a significant improvement when compared to those implemented.
    Description: Dipartimento per la Ptrotezione Civile
    Description: Published
    Description: 1117-1137
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: JCR Journal
    Keywords: Europe ; Earthquake ground motions ; Seismicity and tectonics ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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