ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    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
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2018-03-27
    Description: On 24 August 2016 a magnitude ML 6.0 occurred in the Central Apennines (Italy) between Amatrice and Norcia causing nearly 300 fatalities. The main shock ruptured a NNW‐SSE striking, WSW dipping normal fault. We invert waveforms from 26 three‐component strong motion accelerometers, filtered between 0.02 and 0.5 Hz, within 45 km from the fault. The inferred slip distribution is heterogeneous and characterized by two shallow slip patches updip and NW from the hypocenter, respectively. The rupture history shows bilateral propagation and a relatively high rupture velocity (3.1 km/s). The imaged rupture history produced evident directivity effects both N‐NW and SE of the hypocenter, explaining near‐source peak ground motions. Fault dimensions and peak slip values are large for a moderate‐magnitude earthquake. The retrieved rupture model fits the recorded ground velocities up to 1 Hz, corroborating the effects of rupture directivity and slip heterogeneity on ground shaking and damage pattern.
    Description: Published
    Description: 10745-10752
    Description: 2T. Sorgente Sismica
    Description: JCR Journal
    Keywords: 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 3
    Publication Date: 2017-12-21
    Description: La sequenza sismica che ha seguito il terremoto di magnitudo momento MW= 6.0 di Amatrice del 24 agosto 2016 è la prima sequenza rilevante che avviene da quando, all’inizio del 2015, le modalità di analisi del Bollettino Sismico Italiano (BSI) sono state aggiornate (Nardi et al., 2015). Queste modalità prevedono la pubblicazione del BSI ogni quattro mesi, la revisione solo degli eventi con ML≥ 1.5, la revisione rapida degli eventi con ML≥ 3.5 e l’integrazione all’interno del BSI di tutte le stazioni i cui dati sono archiviati nello European Integrated Data Archive (EIDA). Quest’ultima procedura permette di integrare nel BSI anche le stazioni temporanee (Moretti et al., 2014) installate dal gruppo di emergenza SISMIKO (Sismiko working group, 2016), le cui registrazioni vengono archiviate, in tempi brevi, in EIDA (Mazza et al., 2012) insieme alle stazioni trasmesse in real-time. I quadrimestri del BSI 2015 e il primo del 2016 sono attualmente disponibili (http://cnt.rm.ingv.it/bsi) in formato QuakeML; tale formato contiene le localizzazioni con la stima degli errori, le magnitudo (MW, ML, Md), le letture delle fasi P ed S e i Time Domain Moment Tensor (TDMT). Sono stati inoltre sviluppati alcuni webservices (http://webservices.rm.ingv.it/ws_fdsn.php) per facilitare la lettura dei QuakeML e per rendere il bollettino fruibile alla comunità scientifica. Il terremoto di MW 6.0, avvenuto nella notte del 24 agosto 2016, alle ore 01:36 UTC, nell’area al confine tra l’Umbria, il Lazio, l’Abruzzo e le Marche, ha dato inizio a una sequenza sismica che al 23 settembre 2016 contava circa 11000 eventi.
    Description: Published
    Description: Lecce, Italy
    Description: 4IT. Banche dati
    Keywords: Bollettino Sismico Italiano ; Terremoto Di Amatrice 2016 ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Conference paper
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 4
    Publication Date: 2019-02-19
    Description: The central Italy seismic sequence, started with the Mw = 6.0 Amatrice earthquake on August 24th 2016, is the first significant one after the Italian Seismic Bulletin (BSI) changed its analysis strategies in 2015. These new strategies consist on the release of the BSI every four months, the review of the events with ML ≥ 1.5 and the priority on the review of events with ML ≥ 3.5. Furthermore, in the last year we improved the bulletin tools and made possible the analysis of all the stations whose data are stored in the European Integrated Data Archive (EIDA). The new procedures and software utilities allowed, during the first month of 2016 emergency, to integrate, in the Bulletin, the temporary stations installed by the emergency group SISMIKO, both in real–time transmission and in stand-alone recording. In the early days of the sequence many of the BSI analysts were engaged in the monitoring room shifts, nevertheless at the end of August all events occurred in those days with ML ≥ 4 were analyzed; the largest event recovered and localized is a ML = 4.5 event immediately following the main shock. In September 2016, 83 events with ML ≥ 3.5 were analyzed and re-checked, the number of pickings greatly improved. The focal mechanism of the main shock was evaluated using first motion polarities, and compared with the available Time Domain Moment Tensors and Regional Centroid Moment Tensor. The first eight hours of the day on August 24th, the most critical for the INGV surveillance room, were carefully analyzed: the number of located events increased from 133 to 408. The magnitude of completeness, after the analysis of the BSI, has dropped significantly from about 3.5 to 2.7. The mainshock focal mechanism and the relative locations of the first 8 hours’ aftershocks give clues on the initial fault activation. The seismic sequence in November 2016 is still ongoing; it included a mainshock of Mw = 6.5 on October 30th and 3 events of magnitude greater than 5.0 one on August 24th and two on October 26th.
    Description: Published
    Description: 4IT. Banche dati
    Description: JCR Journal
    Keywords: Central Italy seismic sequence ; Amatrice earthquake August 24th 2016 ; Italian Seismic Bulletin BSI ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 5
    Publication Date: 2017-04-04
    Description: The rapid and accurate information about the ground shaking following an earthquake is necessary for emergency response planning. A prompt strategy is contouring the real data recorded at the stations. However only few regions, i.e. Japan and Taiwan, have an instrumental coverage as good as needed to produce shaking maps relying almost entirely on real data. ShakeMap has been conceived in order to “fill” the data gap and producing stable contouring using the ground motion predictive equations (GMPEs) and site effect. Thus for regions where the data coverage is sparse, the interpolation plays a crucial role and the choice of the GMPE can affect strongly the goodness of the ground shaking estimation. However the GMPEs derive from an empirical regression describing the averaged behavior of the ground shaking and tend to mask, when present, specific trends due to multidimensional effects like the asymmetry of the rupture process (directivity effect). Thus, ShakeMaps for large events may not reproduce faithfully the ground motion in the near source if determined without the introduction of rupture related parameters. One way to improve the ShakeMap prediction is to modify the ground motion modeling in order to better explain the ground motion variability. To this purpose, the empirical model can be refined with information about the rupture process (Spagnuolo PhD2010), in this case using the directivity term defined by Spudich and Chiou (Earthquake Spectra 2008). The aim of this work is to quantify the effectiveness of refined GMPEs in improving the performance of ShakeMap. We quantify the agreement of this new GMPE with the real recorded data, and make inference about the reliability of this new ShakeMap. The test is focused on the study of the ShakeMap degradation when the number of the observations is reduced, and on the quantification of the improvements due to the directivity term. In order to conduct properly the test, we investigate two well- recorded events from Japan: the 2008 Iwate-Miyagi (M7) and the 2000 Tottori (M6.6) events. This work is part of the DPC-INGV S3 project (2007-09), as described in the companion abstract Ameri et al. (ESC2010).
    Description: Published
    Description: Montpellier, France
    Description: 4.1. Metodologie sismologiche per l'ingegneria sismica
    Description: open
    Keywords: ShakeMap ; hazard ; seismology ; directivity
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Conference paper
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 6
    Publication Date: 2021-08-31
    Description: In this article, we present a new data collection that combines information about earthquake damage with seismic shaking. Starting from the Da.D.O. database, which provides information on the damage of individual buildings subjected to sequences of past earthquakes in Italy, we have generated ShakeMaps for all the events with magnitude greater than 5.0 that have contributed to these sequences. The sequences under examination are those of Irpinia 1980, Umbria Marche 1997, Pollino 1998, Molise 2002, L’Aquila 2009 and Emilia 2012. In this way, we were able to combine, for a total of the 117,695 buildings, the engineering parameters included in Da.D.O., but revised and reprocessed in this application, and the ground shaking data for six different variables (namely, in- tensity in MCS scale, PGA, PGV, SA at 0.3s, 1.0s and 3.0s). The potential applications of this data collection are innumerable: from recalibrating fragility curves to training machine learning models to quantifying earthquake damage. This data collection will be made available within Da.D.O., a platform of the Italian Department of Civil Protection, developed by EUCENTRE.
    Description: European project SERA – The Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe; http://www.sera-eu.org/en/home/; GA 730900.
    Description: Published
    Description: 36-51
    Description: 5T. Sismologia, geofisica e geologia per l'ingegneria sismica
    Description: N/A or not JCR
    Keywords: ShakeMap ; Damage ; Data Collection ; 05.08. Risk ; 05.02. Data dissemination ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 7
    Publication Date: 2022-02-11
    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, achieving human-like performance under certain circumstances. However, as studies differ in the datasets and evaluation tasks, it is unclear how the different approaches compare to each other. Furthermore, there are no systematic studies about model performance in cross-domain scenarios, that is, when applied to data with different characteristics. Here, we address these questions by conducting a large-scale benchmark. We compare six previously published deep learning models on eight data sets 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 a small advantage for EQTransformer on teleseismic data. Furthermore, we conduct a cross-domain study, analyzing model performance on data sets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but models trained on regional data do not transfer well to teleseismic data. 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 evaluate new models or performance on new data sets. Furthermore, we make all trained models available through the SeisBench framework, giving end-users an easy way to apply these models.
    Description: This work was supported by the Helmholtz Association Initiative and Networking Fund on the HAICORE@KIT partition. J. Münchmeyer acknowledges the support of the Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS). The authors thank the Impuls-und Vernetzungsfonds of the HGF to support the REPORT-DL project under the grant agreement ZT-I-PF-5-53. This work was also 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 e allo sviluppo del Paese, legge 145/2018.” Open access funding enabled and organized by Projekt DEAL.
    Description: Published
    Description: e2021JB023499
    Description: 3T. Fisica dei terremoti e Sorgente Sismica
    Description: JCR Journal
    Keywords: seismic phase recognition ; deep learnig ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 8
    Publication Date: 2021-12-14
    Description: The Italian earthquake waveform data are collected here in a dataset suited for machine learning analysis (ML) applications. The dataset consists of nearly 1.2 million three-component (3C) waveform traces from about 50 000 earthquakes and more than 130 000 noise 3C waveform traces, for a total of about 43 000 h of data and an average of 21 3C traces provided per event. The earthquake list is based on the Italian Seismic Bulletin (http://terremoti.ingv.it/bsi, last access: 15 February 2020​​​​​​​) of the Istituto Nazionale di Geofisica e Vulcanologia between January 2005 and January 2020, and it includes events in the magnitude range between 0.0 and 6.5. The waveform data have been recorded primarily by the Italian National Seismic Network (network code IV) and include both weak- (HH, EH channels) and strong-motion (HN channels) recordings. All the waveform traces have a length of 120 s, are sampled at 100 Hz, and are provided both in counts and ground motion physical units after deconvolution of the instrument transfer functions. The waveform dataset is accompanied by metadata consisting of more than 100 parameters providing comprehensive information on the earthquake source, the recording stations, the trace features, and other derived quantities. This rich set of metadata allows the users to target the data selection for their own purposes. Much of these metadata can be used as labels in ML analysis or for other studies. The dataset, assembled in HDF5 format, is available at http://doi.org/10.13127/instance (Michelini et al., 2021).
    Description: Published
    Description: 5509–5544
    Description: 4T. Sismicità dell'Italia
    Description: JCR Journal
    Keywords: 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 9
    Publication Date: 2022-12-01
    Description: We present a web portal for the prompt visualization of the maps of ground shaking generated using the USGS ShakeMap 4 software (Worden et al., 2020). The web interface renders the standard products provided by ShakeMap dynamically (using Leaflet) and statically (standard shakemaps). The information included in the dynamic maps can be onfigured through different overlays. The dual view rendering modality allows presenting side-by-side maps of different intensity measurements. In addition, for each earthquake, it is possible to download all the data that contributed to the calculation, together with information on the seismological models adopted. The appearance of the web portal is easily configurable by replacing the logo and banners. The software can be installed both on laptops and on server computers. The user can opt between the docker image or installation of the software after installation of a web server (e.g., NGINX or Apache).
    Description: Published
    Description: 3481–3488
    Description: 8T. Sismologia in tempo reale e Early Warning Sismico e da Tsunami
    Description: JCR Journal
    Keywords: shaking ; intensity ; macroseismic ; impact ; terremoto ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 10
    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
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...