Articles | Volume 13, issue 12
https://doi.org/10.5194/essd-13-5509-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-5509-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
30 Nov 2021
Data description paper |
| 30 Nov 2021
| 30 Nov 2021
INSTANCE – the Italian seismic dataset for machine learning
Alberto Michelini
CORRESPONDING AUTHOR
Istituto Nazionale di Geofisica e Vulcanologia, via di Vigna Murata, 605, 00143 Rome, Italy
Spina Cianetti
Istituto Nazionale di Geofisica e Vulcanologia, via Cesare Battisti, 53, Pisa, Italy
Sonja Gaviano
Dipartimento di Scienze della Terra, Unversità degli Studi di Firenze, Via La Pira 4, Florence, Italy
Istituto Nazionale di Geofisica e Vulcanologia, via Cesare Battisti, 53, Pisa, Italy
Carlo Giunchi
Istituto Nazionale di Geofisica e Vulcanologia, via Cesare Battisti, 53, Pisa, Italy
Dario Jozinović
Istituto Nazionale di Geofisica e Vulcanologia, via di Vigna Murata, 605, 00143 Rome, Italy
Dipartimento di Scienze, Unversità degli Studi Roma Tre, Largo San Leonardo Murialdo 1, Rome, Italy
Valentino Lauciani
Istituto Nazionale di Geofisica e Vulcanologia, via di Vigna Murata, 605, 00143 Rome, Italy
Related authors
Ilaria Oliveti, Licia Faenza, and Alberto Michelini
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-372, https://doi.org/10.5194/essd-2020-372, 2020
Revised manuscript not accepted
Short summary
Short summary
This research makes available an updated and homogenous set of data related to Italian earthquakes for assessing parameters in terms of recording, event and station distributions as well as macroseismic intensity degrees. The work required the intersection of different sources and is expected to accelerate research progress in the field of hazard assessment, creation of near-real-time maps of ground motion and shaking intensity and calibration of relationships between earthquake parameters.
Alberto Michelini, Lucia Margheriti, Marco Cattaneo, Gianpaolo Cecere, Giuseppe D'Anna, Alberto Delladio, Milena Moretti, Stefano Pintore, Alessandro Amato, Alberto Basili, Andrea Bono, Paolo Casale, Peter Danecek, Martina Demartin, Licia Faenza, Valentino Lauciani, Alfonso Giovanni Mandiello, Alessandro Marchetti, Carlo Marcocci, Salvatore Mazza, Francesco Mariano Mele, Anna Nardi, Concetta Nostro, Maurizio Pignone, Matteo Quintiliani, Sandro Rao, Laura Scognamiglio, and Giulio Selvaggi
Adv. Geosci., 43, 31–38, https://doi.org/10.5194/adgeo-43-31-2016, https://doi.org/10.5194/adgeo-43-31-2016, 2016
Short summary
Short summary
The Istituto Nazionale di Geofisica e Vulcanologia runs the Italian National Seismic Network (about 400 stations, seismometers, accelerometers and GPS antennas) and other networks at national scale for monitoring earthquakes and tsunami as a part of the National Civil Protection System coordinated by the Italian Department of Civil Protection. This work summarises the acquisition and the distribution of the data and the analysis that are carried out for seismic surveillance and tsunami alert.
F. Bernardi, A. Lomax, A. Michelini, V. Lauciani, A. Piatanesi, and S. Lorito
Nat. Hazards Earth Syst. Sci., 15, 2019–2036, https://doi.org/10.5194/nhess-15-2019-2015, https://doi.org/10.5194/nhess-15-2019-2015, 2015
Ilaria Oliveti, Licia Faenza, and Alberto Michelini
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-372, https://doi.org/10.5194/essd-2020-372, 2020
Revised manuscript not accepted
Short summary
Short summary
This research makes available an updated and homogenous set of data related to Italian earthquakes for assessing parameters in terms of recording, event and station distributions as well as macroseismic intensity degrees. The work required the intersection of different sources and is expected to accelerate research progress in the field of hazard assessment, creation of near-real-time maps of ground motion and shaking intensity and calibration of relationships between earthquake parameters.
Alberto Michelini, Lucia Margheriti, Marco Cattaneo, Gianpaolo Cecere, Giuseppe D'Anna, Alberto Delladio, Milena Moretti, Stefano Pintore, Alessandro Amato, Alberto Basili, Andrea Bono, Paolo Casale, Peter Danecek, Martina Demartin, Licia Faenza, Valentino Lauciani, Alfonso Giovanni Mandiello, Alessandro Marchetti, Carlo Marcocci, Salvatore Mazza, Francesco Mariano Mele, Anna Nardi, Concetta Nostro, Maurizio Pignone, Matteo Quintiliani, Sandro Rao, Laura Scognamiglio, and Giulio Selvaggi
Adv. Geosci., 43, 31–38, https://doi.org/10.5194/adgeo-43-31-2016, https://doi.org/10.5194/adgeo-43-31-2016, 2016
Short summary
Short summary
The Istituto Nazionale di Geofisica e Vulcanologia runs the Italian National Seismic Network (about 400 stations, seismometers, accelerometers and GPS antennas) and other networks at national scale for monitoring earthquakes and tsunami as a part of the National Civil Protection System coordinated by the Italian Department of Civil Protection. This work summarises the acquisition and the distribution of the data and the analysis that are carried out for seismic surveillance and tsunami alert.
A. Govoni, L. Margheriti, M. Moretti, V. Lauciani, G. Sensale, A. Bucci, and F. Criscuoli
Adv. Geosci., 41, 35–42, https://doi.org/10.5194/adgeo-41-35-2015, https://doi.org/10.5194/adgeo-41-35-2015, 2015
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We designed, setup and tested on the field an 'easy to deploy' temporary UMTS real-time seismic network resolving or minimizing the main drawbacks: Internet security, signal and service availability, power consumption.Overall this solution has proved to be a very cost effective approach with real-time data acquisition rates usually greater than 97% and all the benefits that result from the fast integration of the temporary data in the National Network monitoring system and in the EIDA data bank.
F. Bernardi, A. Lomax, A. Michelini, V. Lauciani, A. Piatanesi, and S. Lorito
Nat. Hazards Earth Syst. Sci., 15, 2019–2036, https://doi.org/10.5194/nhess-15-2019-2015, https://doi.org/10.5194/nhess-15-2019-2015, 2015
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Michal Kruszewski, Gerd Klee, Thomas Niederhuber, and Oliver Heidbach
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The authors assemble an in situ stress magnitude and orientation database based on 429 hydrofracturing tests that were carried out in six coal mines and two coal bed methane boreholes between 1986 and 1995 within the greater Ruhr region (Germany). Our study summarises the results of the extensive in situ stress test campaign and assigns quality to each data record using the established quality ranking schemes of the World Stress Map project.
Andrea Rovida, Andrea Antonucci, and Mario Locati
Earth Syst. Sci. Data, 14, 5213–5231, https://doi.org/10.5194/essd-14-5213-2022, https://doi.org/10.5194/essd-14-5213-2022, 2022
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EPICA is the 1000–1899 catalogue compiled for the European Seismic Hazard Model 2020 and contains 5703 earthquakes with Mw ≥ 4.0. It relies on the data of the European Archive of Historical Earthquake Data (AHEAD), both macroseismic intensities from historical seismological studies and parameters from regional catalogues. For each earthquake, the most representative datasets were selected and processed in order to derive harmonised parameters, both from intensity data and parametric catalogues.
Suqin Zhang, Changhua Fu, Jianjun Wang, Guohao Zhu, Chuanhua Chen, Shaopeng He, Pengkun Guo, and Guoping Chang
Earth Syst. Sci. Data, 14, 5195–5212, https://doi.org/10.5194/essd-14-5195-2022, https://doi.org/10.5194/essd-14-5195-2022, 2022
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The Sheshan observatory has nearly 150 years of observation history, and its observation data have important scientific value. However, with time, these precious historical data face the risk of damage and loss. We have carried out a series of rescues on the historical data of the Sheshan observatory. New historical datasets were released, including the quality-controlled absolute hourly mean values of three components (D, H, and Z) from 1933 to 2019.
Guoyu Li, Wei Ma, Fei Wang, Huijun Jin, Alexander Fedorov, Dun Chen, Gang Wu, Yapeng Cao, Yu Zhou, Yanhu Mu, Yuncheng Mao, Jun Zhang, Kai Gao, Xiaoying Jin, Ruixia He, Xinyu Li, and Yan Li
Earth Syst. Sci. Data, 14, 5093–5110, https://doi.org/10.5194/essd-14-5093-2022, https://doi.org/10.5194/essd-14-5093-2022, 2022
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A permafrost monitoring network was established along the China–Russia crude oil pipeline (CRCOP) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. The resulting datasets fill the gaps in the spatial coverage of mid-latitude mountain permafrost databases. Results show that permafrost warming has been extensively observed along the CRCOP route, and local disturbances triggered by the CRCOPs have resulted in significant permafrost thawing.
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
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This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Xiaoli Chang, Huijun Jin, Ruixia He, Yanlin Zhang, Xiaoying Li, Xiaoying Jin, and Guoyu Li
Earth Syst. Sci. Data, 14, 3947–3959, https://doi.org/10.5194/essd-14-3947-2022, https://doi.org/10.5194/essd-14-3947-2022, 2022
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Based on 10-year observations of ground temperatures in seven deep boreholes in Gen’he, Mangui, and Yituli’he, a wide range of mean annual ground temperatures at the depth of 20 m (−2.83 to −0.49 ℃) and that of annual maximum thawing depth (about 1.1 to 7.0 m) have been revealed. This study demonstrates that most trajectories of permafrost changes in Northeast China are ground warming and permafrost degradation, except that the shallow permafrost is cooling in Yituli’he.
Alice C. Frémand, Julien A. Bodart, Tom A. Jordan, Fausto Ferraccioli, Carl Robinson, Hugh F. J. Corr, Helen J. Peat, Robert G. Bingham, and David G. Vaughan
Earth Syst. Sci. Data, 14, 3379–3410, https://doi.org/10.5194/essd-14-3379-2022, https://doi.org/10.5194/essd-14-3379-2022, 2022
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This paper presents the release of large swaths of airborne geophysical data (including gravity, magnetics, and radar) acquired between 1994 and 2020 over Antarctica by the British Antarctic Survey. These include a total of 64 datasets from 24 different surveys, amounting to >30 % of coverage over the Antarctic Ice Sheet. This paper discusses how these data were acquired and processed and presents the methods used to standardize and publish the data in an interactive and reproducible manner.
Václav Vavryčuk, Petra Adamová, Jana Doubravová, and Josef Horálek
Earth Syst. Sci. Data, 14, 2179–2194, https://doi.org/10.5194/essd-14-2179-2022, https://doi.org/10.5194/essd-14-2179-2022, 2022
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We present a unique catalogue of more than 5100 highly accurate seismic moment tensors of earthquakes that occurred in West Bohemia, Czech Republic, in the period 2008–2018. The catalogue covers a long period of seismicity with several prominent earthquake swarms. The dataset is ideal for being utilized by a large community of researchers for various seismological purposes such as for studies of migration of foci, spatiotemporal evolution of seismicity, tectonic stress, or fluid flow on faults.
Domenico Di Giacomo and Dmitry A. Storchak
Earth Syst. Sci. Data, 14, 393–409, https://doi.org/10.5194/essd-14-393-2022, https://doi.org/10.5194/essd-14-393-2022, 2022
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The surface wave magnitude Ms is the only magnitude type that can be computed since the dawn of modern observational seismology (beginning
of the last century) for most shallow earthquakes worldwide. As a result of a 10+ year effort to digitize pre-1971 measurements of surface wave amplitudes and periods from printed bulletins, we are able to recompute Ms using a large set of stations and obtain it for the first time for several hundred earthquakes.
Mark Jessell, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko
Earth Syst. Sci. Data, 14, 381–392, https://doi.org/10.5194/essd-14-381-2022, https://doi.org/10.5194/essd-14-381-2022, 2022
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To robustly train and test automated methods in the geosciences, we need to have access to large numbers of examples where we know
the answer. We present a suite of synthetic 3D geological models with their gravity and magnetic responses that allow researchers to test their methods on a whole range of geologically plausible models, thus overcoming one of the fundamental limitations of automation studies.
Ulysse Lebrec, Victorien Paumard, Michael J. O'Leary, and Simon C. Lang
Earth Syst. Sci. Data, 13, 5191–5212, https://doi.org/10.5194/essd-13-5191-2021, https://doi.org/10.5194/essd-13-5191-2021, 2021
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This paper presents an integrated workflow that builds on satellite images and 3D seismic surveys, integrated with historical depth soundings, to generate regional high-resolution digital elevation models (DEMs). The workflow was applied to the North West Shelf of Australia and led to the creation of new DEMs, with a resolution of 10 × 10 m in nearshore areas and 30 × 30 m elsewhere over an area of nearly 1 000 000 km2. This constitutes a major improvement of the pre-existing 250 × 250 m DEM.
Xiangjin Meng, Kebiao Mao, Fei Meng, Jiancheng Shi, Jiangyuan Zeng, Xinyi Shen, Yaokui Cui, Lingmei Jiang, and Zhonghua Guo
Earth Syst. Sci. Data, 13, 3239–3261, https://doi.org/10.5194/essd-13-3239-2021, https://doi.org/10.5194/essd-13-3239-2021, 2021
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In order to improve the accuracy of China's regional agricultural drought monitoring and climate change research, we produced a long-term series of soil moisture products by constructing a time and depth correction model for three soil moisture products with the help of ground observation data. The spatial resolution is improved by building a spatial weight decomposition model, and validation indicates that the new product can meet application needs.
Alexis Neven, Pradip Kumar Maurya, Anders Vest Christiansen, and Philippe Renard
Earth Syst. Sci. Data, 13, 2743–2752, https://doi.org/10.5194/essd-13-2743-2021, https://doi.org/10.5194/essd-13-2743-2021, 2021
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The shallow underground is constituted of sediments that present high spatial variability. This upper layer is the most extensively used for resource exploitation (groundwater, geothermal heat, construction materials, etc.). Understanding and modeling the spatial variability of these deposits is crucial. We present a high-resolution electrical resistivity dataset that covers the upper Aare Valley in Switzerland. These data can help develop methods to characterize these geological formations.
Angela Saraò, Monica Sugan, Gianni Bressan, Gianfranco Renner, and Andrea Restivo
Earth Syst. Sci. Data, 13, 2245–2258, https://doi.org/10.5194/essd-13-2245-2021, https://doi.org/10.5194/essd-13-2245-2021, 2021
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Focal mechanisms describe the orientation of the fault on which an earthquake occurs and the slip direction. They are necessary to understand seismotectonic processes and for seismic hazard analysis. We present a focal mechanism catalogue of 772 selected earthquakes of
1.8 ≤ M ≤ 6.5 that occurred in the southeastern Alps and surrounding areas from 1928 to 2019. For each earthquake, we report focal mechanisms from the literature and newly computed solutions, and we suggest a preferred one.
Pavol Zahorec, Juraj Papčo, Roman Pašteka, Miroslav Bielik, Sylvain Bonvalot, Carla Braitenberg, Jörg Ebbing, Gerald Gabriel, Andrej Gosar, Adam Grand, Hans-Jürgen Götze, György Hetényi, Nils Holzrichter, Edi Kissling, Urs Marti, Bruno Meurers, Jan Mrlina, Ema Nogová, Alberto Pastorutti, Corinne Salaun, Matteo Scarponi, Josef Sebera, Lucia Seoane, Peter Skiba, Eszter Szűcs, and Matej Varga
Earth Syst. Sci. Data, 13, 2165–2209, https://doi.org/10.5194/essd-13-2165-2021, https://doi.org/10.5194/essd-13-2165-2021, 2021
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The gravity field of the Earth expresses the overall effect of the distribution of different rocks at depth with their distinguishing densities. Our work is the first to present the high-resolution gravity map of the entire Alpine orogen, for which high-quality land and sea data were reprocessed with the exact same calculation procedures. The results reflect the local and regional structure of the Alpine lithosphere in great detail. The database is hereby openly shared to serve further research.
Natalia Sergeyeva, Alexei Gvishiani, Anatoly Soloviev, Lyudmila Zabarinskaya, Tamara Krylova, Mikhail Nisilevich, and Roman Krasnoperov
Earth Syst. Sci. Data, 13, 1987–1999, https://doi.org/10.5194/essd-13-1987-2021, https://doi.org/10.5194/essd-13-1987-2021, 2021
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The K index is the classical, commonly used parameter of geomagnetic activity that serves as the measure of local magnetic field variations. This paper presents a unique collection of historical K index values that was formed at the World Data Center for Solar-Terrestrial Physics in Moscow. It includes the results of the K index determination at 41 geomagnetic observatories of the former USSR for the period from July 1957 to the early 1990s.
Domenico Di Giacomo, James Harris, and Dmitry A. Storchak
Earth Syst. Sci. Data, 13, 1957–1985, https://doi.org/10.5194/essd-13-1957-2021, https://doi.org/10.5194/essd-13-1957-2021, 2021
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We provide a comprehensive overview of the content in terms of moment magnitude (Mw) in the Bulletin of the International Seismological Centre (ISC). Mw is the preferred magnitude to characterize earthquakes in various research topics (e.g. Earth seismicity rates) and other applications (e.g. seismic hazard). We describe first the contribution of global agencies and agencies operating at a regional scale and then discuss features of Mw via different sets of comparisons.
Irene DeFelipe, Juan Alcalde, Monika Ivandic, David Martí, Mario Ruiz, Ignacio Marzán, Jordi Diaz, Puy Ayarza, Imma Palomeras, Jose-Luis Fernandez-Turiel, Cecilia Molina, Isabel Bernal, Larry Brown, Roland Roberts, and Ramon Carbonell
Earth Syst. Sci. Data, 13, 1053–1071, https://doi.org/10.5194/essd-13-1053-2021, https://doi.org/10.5194/essd-13-1053-2021, 2021
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Seismic data provide critical information about the structure of the lithosphere, and their preservation is essential for innovative research reusing data. The Seismic DAta REpository (SeisDARE) comprises legacy and recently acquired seismic data in the Iberian Peninsula and Morocco. This database has been built by a network of different institutions that promote multidisciplinary research. We aim to make seismic data easily available to the research, industry, and educational communities.
Anna L. Morozova, Paulo Ribeiro, and M. Alexandra Pais
Earth Syst. Sci. Data, 13, 809–825, https://doi.org/10.5194/essd-13-809-2021, https://doi.org/10.5194/essd-13-809-2021, 2021
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The Coimbra Magnetic Observatory (COI), Portugal, established in 1866, has provided nearly continuous records of the geomagnetic field for more than 150 years. However, during its long lifetime inevitable changes to the instruments and measurement procedures and even the relocation of the observatory have taken place. Such changes affect the quality of the measurements, introducing false (artificial) variations. We analyzed COI historical data to find and correct such artificial variations.
Abdelrazek Elnashar, Linjiang Wang, Bingfang Wu, Weiwei Zhu, and Hongwei Zeng
Earth Syst. Sci. Data, 13, 447–480, https://doi.org/10.5194/essd-13-447-2021, https://doi.org/10.5194/essd-13-447-2021, 2021
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Based on a site-pixel validation and comparison of different global evapotranspiration (ET) products, this paper aims to produce a synthesized ET which has a minimum level of uncertainty over as many conditions as possible from 1982 to 2019. Through a high-quality flux eddy covariance (EC) covering the globe, PML, SSEBop, MOD16A2105, and NTSG ET products were chosen to create the new dataset. It agreed well with flux EC ET and can be used without other datasets or further assessments.
Jana Lasser, Joanna M. Nield, and Lucas Goehring
Earth Syst. Sci. Data, 12, 2881–2898, https://doi.org/10.5194/essd-12-2881-2020, https://doi.org/10.5194/essd-12-2881-2020, 2020
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The publication presents six data sets that describe the surface and subsurface characteristics of salt deserts in southern California. The data were collected during two field studies in 2016 and 2018 and are used to investigate the origins of the eye-catching hexagonal salt ridge patterns that emerge in such deserts. It is important to understand how these salt crusts grow since these deserts and their dynamic surface structure play a major role in the emission of dust into the atmosphere.
Roman Krasnoperov, Dmitry Peregoudov, Renata Lukianova, Anatoly Soloviev, and Boris Dzeboev
Earth Syst. Sci. Data, 12, 555–561, https://doi.org/10.5194/essd-12-555-2020, https://doi.org/10.5194/essd-12-555-2020, 2020
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The paper presents a collection of magnetic field measurements performed by early Soviet magnetic satellite missions Kosmos-49 (1964) and Kosmos-321 (1970). These data were used as initial data for analysis of the structure of the Earth’s magnetic field sources and for compilation of a series of its analytical models. The most notable model that employed Kosmos-49 data was the first generation of the International Geomagnetic Reference Field for epoch 1965.0.
Tony Alfredo Stabile, Vincenzo Serlenga, Claudio Satriano, Marco Romanelli, Erwan Gueguen, Maria Rosaria Gallipoli, Ermann Ripepi, Jean-Marie Saurel, Serena Panebianco, Jessica Bellanova, and Enrico Priolo
Earth Syst. Sci. Data, 12, 519–538, https://doi.org/10.5194/essd-12-519-2020, https://doi.org/10.5194/essd-12-519-2020, 2020
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This paper presents data collected by a seismic network developed in the framework of the INSIEME project aimed to study induced seismicity processes. The network is composed of eight stations deployed around two clusters of induced microearthquakes in the High Agri Valley (southern Italy). The solutions for reducing the background noise level are presented and the quality of acquired data is discussed. Such open-access data can be used by the scientific community for different applications.
Konstantinos Lentas, Domenico Di Giacomo, James Harris, and Dmitry A. Storchak
Earth Syst. Sci. Data, 11, 565–578, https://doi.org/10.5194/essd-11-565-2019, https://doi.org/10.5194/essd-11-565-2019, 2019
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In this article we try to make the broad geoscience community and especially the seismological community aware of the availability of earthquake source mechanisms in the Bulletin of the International Seismological Centre (ISC) and encourage researchers to make use of this data set in future research. Moreover, we acknowledge the data providers, and we encourage others to routinely submit their source mechanism solutions to the ISC.
Xuanmei Fan, Gianvito Scaringi, Guillem Domènech, Fan Yang, Xiaojun Guo, Lanxin Dai, Chaoyang He, Qiang Xu, and Runqiu Huang
Earth Syst. Sci. Data, 11, 35–55, https://doi.org/10.5194/essd-11-35-2019, https://doi.org/10.5194/essd-11-35-2019, 2019
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Large earthquakes cause major disturbances to mountain landscapes. They trigger many landslides that can form deposits of debris on steep slopes and channels. Rainfall can remobilise these deposits and generate large and destructive flow-like landslides and floods. We release two datasets that track a decade of landsliding following the 2008 7.9 magnitude Wenchuan earthquake in China. These data are useful for quantifying the role of major earthquakes in shaping mountain landscapes.
Domenico Di Giacomo, E. Robert Engdahl, and Dmitry A. Storchak
Earth Syst. Sci. Data, 10, 1877–1899, https://doi.org/10.5194/essd-10-1877-2018, https://doi.org/10.5194/essd-10-1877-2018, 2018
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We outline work done to improve and extend the new reference catalogue of global earthquakes instrumentally recorded since 1904, the ISC-GEM Catalogue. We have added thousands of earthquakes between 1904 and 1959 and in recent years compared to the 2013 release. As earthquake catalogues are widely used for different aspects of research, we believe that this dataset will be instrumental for years to come for researchers involved in studies on seismic hazard and patterns of the Earth's seismicity.
Laura Sánchez, Christof Völksen, Alexandr Sokolov, Herbert Arenz, and Florian Seitz
Earth Syst. Sci. Data, 10, 1503–1526, https://doi.org/10.5194/essd-10-1503-2018, https://doi.org/10.5194/essd-10-1503-2018, 2018
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We provide a surface-kinematics model for the Alpine region based on high-level data analysis of 300 geodetic stations continuously operating over 12.4 years. This model includes a deformation model, a continuous velocity field, and a strain field consistently assessed for the entire Alpine mountain belt. Horizontal and vertical motion patterns are clearly identified and supported by uncertainties better than ±0.2 mm a−1 and ±0.3 mm a−1 in the horizontal and vertical components, respectively.
Ingo Sasgen, Alba Martín-Español, Alexander Horvath, Volker Klemann, Elizabeth J. Petrie, Bert Wouters, Martin Horwath, Roland Pail, Jonathan L. Bamber, Peter J. Clarke, Hannes Konrad, Terry Wilson, and Mark R. Drinkwater
Earth Syst. Sci. Data, 10, 493–523, https://doi.org/10.5194/essd-10-493-2018, https://doi.org/10.5194/essd-10-493-2018, 2018
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We present a collection of data sets, consisting of surface-elevation rates for Antarctic ice sheet from a combination of Envisat and ICESat, bedrock uplift rates for 118 GPS sites in Antarctica, and optimally filtered GRACE gravity field rates. We provide viscoelastic response functions to a disc load forcing for Earth structures present in East and West Antarctica. This data collection enables a joint inversion for present-day ice-mass changes and glacial isostatic adjustment in Antarctica.
Kristian Kjellerup Kjeldsen, Reimer Wilhelm Weinrebe, Jørgen Bendtsen, Anders Anker Bjørk, and Kurt Henrik Kjær
Earth Syst. Sci. Data, 9, 589–600, https://doi.org/10.5194/essd-9-589-2017, https://doi.org/10.5194/essd-9-589-2017, 2017
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Here we present bathymetric and hydrographic measurements from two fjords in southeastern Greenland surveyed in 2014, leading to improved knowledge of the fjord morphology and an assessment of the variability in water masses in the fjords systems. Data were collected as part of a larger field campaign in which we targeted marine and terrestrial observations to assess the long-term behavior of the Greenland ice sheet and provide linkages to modern observations.
Johannes Petrone, Gustav Sohlenius, Emma Johansson, Tobias Lindborg, Jens-Ove Näslund, Mårten Strömgren, and Lars Brydsten
Earth Syst. Sci. Data, 8, 663–677, https://doi.org/10.5194/essd-8-663-2016, https://doi.org/10.5194/essd-8-663-2016, 2016
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This paper presents data and resulting models of spatial distributions of maximum active layer thickness and sediment thickness and their connection to surface vegetation and topography from the Kangerlussuaq region, western Greenland. The data set constitutes geometrical information and will be used in coupled hydrological and biogeochemical modeling together with previous published hydrological data (doi:10.5194/essd-7-93-2015, 2015) and biogeochemical data (doi:10.5194/essd-8-439-2016, 2016).
B. K. Biskaborn, J.-P. Lanckman, H. Lantuit, K. Elger, D. A. Streletskiy, W. L. Cable, and V. E. Romanovsky
Earth Syst. Sci. Data, 7, 245–259, https://doi.org/10.5194/essd-7-245-2015, https://doi.org/10.5194/essd-7-245-2015, 2015
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This paper introduces the new database of the Global Terrestrial Network for Permafrost (GTN-P) on permafrost temperature and active layer thickness data. It describes the operability of the Data Management System and the data quality. By applying statistics on GTN-P metadata, we analyze the spatial sample representation of permafrost monitoring sites. Comparison with environmental variables and climate projection data enable identification of potential future research locations.
P. Arason, G. N. Petersen, and H. Bjornsson
Earth Syst. Sci. Data, 3, 9–17, https://doi.org/10.5194/essd-3-9-2011, https://doi.org/10.5194/essd-3-9-2011, 2011
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado,
G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp,
A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M.,
Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C.,
Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P.,
Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P.,
Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow:
Large-Scale Machine Learning on Heterogeneous Distributed
Systems, arXiv [preprint], arXiv:1603.04467, 14 March 2016. a
Alavi, A. H.: Prediction of principal ground-motion parameters using a hybrid
method coupling artificial neural networks and simulated annealing, Comput. Struct., 89, 2176–2194, https://doi.org/10.1016/j.compstruc.2011.08.019,
2011. a
Baig, A. M., Campillo, M., and Brenguier, F.: Denoising seismic noise cross
correlations, J. Geophys. Res., 114, B08310,
https://doi.org/10.1029/2008JB006085, 2009. a
Bergen, K. J., Johnson, P. A., de Hoop, M. V., and Beroza, G. C.: Machine
learning for data-driven discovery in solid Earth geoscience, Science, 363, eaau0323, https://doi.org/10.1126/science.aau0323, 2019. a
Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., and Wassermann,
J.: ObsPy: A Python Toolbox for Seismology, Seismol. Res.
Lett., 81, 530–533, https://doi.org/10.1785/gssrl.81.3.530, 2010. a, b
Bono, A.: SisPick! 2.0 Sistema interattivo per l'interpretazione di segnali
sismici, Manuale utente, Tech. Rep. Rapporti tecnici, n. 59, INGV, Rome,
Italy, available at: http://editoria.rm.ingv.it/archivio_pdf/rapporti/58/pdf/rapporti_59.pdf (last access: 30 August 2021),
2008. a
Boore, D. M.: Estimating Vs(30) (or NEHRP Site Classes) from Shallow
Velocity Models (Depths < 30 m), B.
Seismol. Soc. Am., 94, 591–597, https://doi.org/10.1785/0120030105,
2004. a
Buland, R.: SEED Reference Manual, Tech. Rep., Incorporated Research
Institutions for Seismology (IRIS), available at:
http://www.fdsn.org/pdf/SEEDManual_V2.4.pdf (last access: 30 August 2021), 2006. a
Chen, Y., Zhang, M., Bai, M., and Chen, W.: Improving the Signal-to-Noise
Ratio of Seismological Datasets by Unsupervised Machine Learning,
Seismol. Res. Lett., 90, 1552–1564, https://doi.org/10.1785/0220190028,
2019. a
Chollet, F. and others: Keras [code], available at: https://keras.io (last access: 25 November 2021), 2015. a
Danecek, P., Pintore, S., Mazza, S., Mandiello, A., Fares, M., Carluccio, I.,
Della Bina, E., Franceschi, D., Moretti, M., Lauciani, V., Quintiliani, M.,
and Michelini, A.: The Italian Node of the European Integrated Data
Archive, Seismol. Res. Lett., 92, 1726–1737, https://doi.org/10.1785/0220200409, 2021. a, b, c, d, e
Del Pezzo, E., Esposito, A., Giudicepietro, F., Marinaro, M., Martini, M., and
Scarpetta, S.: Discrimination of Earthquakes and Underwater Explosions
Using Neural Networks, B. Seismol. Soc.
Am., 93, 215–223, https://doi.org/10.1785/0120020005, 2003. a
Derras, B.: Peak Ground Acceleration Prediction Using Artificial
Neural Networks Approach: Application to the Kik-Net Data,
International Journal of Earthquake Engineering and Hazard Mitigation, 2, 144–153, https://doi.org/10.15866/irehm.v2i4.7121, 2014. a
Derras, B., Bard, P., Cotton, F., and Bekkouche, A.: Adapting the Neural
Network Approach to PGA Prediction: An Example Based on the
KiK‐net Data, B. Seismol. Soc. Am., 102,
1446–1461, https://doi.org/10.1785/0120110088, 2012. a
Dramsch, J. S.: Chapter One – 70 years of machine learning in geoscience in
review, Adv. Geophys., 61, 1–55, https://doi.org/10.1016/bs.agph.2020.08.002, 2020. a
EMERSITO Working Group: Rete sismica del gruppo EMERSITO, sequenza sismica
del 2016 in Italia Centrale, Instituto Nazionale di Geofisica e Vulcanologia [data set], https://doi.org/10.13127/SD/7TXEGDO5X8, 2018. a
Esposito, A. M., Giudicepietro, F., Scarpetta, S., D'Auria, L., Marinaro, M.,
and Martini, M.: Automatic Discrimination among Landslide,
Explosion-Quake, and Microtremor Seismic Signals at Stromboli
Volcano Using Neural Networks, B. Seismol. Soc.
Am., 96, 1230–1240, https://doi.org/10.1785/0120050097, 2006. a
Fang, L., Wu, Z., and Song, K.: SeismOlympics, Seismol. Res.
Lett., 88, 1429–1430, https://doi.org/10.1785/0220170134, 2017. a
Geological Survey-Provincia Autonoma di Trento: Trentino Seismic Network, International Federation of Digital Seismograph Networks [data set],
https://doi.org/10.7914/SN/ST, 1981. a
Giardini, D., Wiemer, S., Faeh, D., and Deichmann, N.: Seismic Hazard
Assessment of Switzerland, 2004, Tech. Rep., Swiss Seismological Service,
ETH Zurich, available at:
http://www.seismo.ethz.ch/export/sites/sedsite/knowledge/.galleries/pdf_hazard2004/hazard_report_2004.pdf_2063069299.pdf (last access: 25 November 2021),
2004. a
Gulia, L. and Gasperini, P.: Contamination of Frequency–Magnitude Slope
(b‐Value) by Quarry Blasts: An Example for Italy, Seismol.
Res. Lett., 92, 3538–3551, https://doi.org/10.1785/0220210080, 2021. a, b
Gutenberg, B. and Richter, C. F.: Frequency of earthquakes in California,
B. Seismol. Soc. Am., 34, 185–188, 1944. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P.,
Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy,
T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array
programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a, b
Ingate, S.: The IRIS Consortium: Community Based Facilities and Data Management for Seismology, in: Earthquake Monitoring and Seismic Hazard Mitigation in Balkan Countries, NATO Science Series: IV: Earth and Environmental Sciences, edited by: Husebye, E. S., vol. 81, 121–132, Springer, Dordrecht, https://doi.org/10.1007/978-1-4020-6815-7_8, 2008. a
INGV Seismological Data Centre: Rete Sismica Nazionale (RSN), Instituto Nazionale di Geofisica e Vulcanologia [data set],
https://doi.org/10.13127/SD/X0FXNH7QFY, 1997. a
Institute of Geosciences, Energy, Water and Environment: Albanian
Seismological Network, International Federation of Digital Seismograph Networks [data set], https://doi.org/10.7914/SN/AC, 2002. a
Istituto Nazionale di Geofisica e Vulcanologia (INGV): INGV experiments
network, Instituto Nazionale di Geofisica e Vulcanologia [data set], available at: http://cnt.rm.ingv.it/instruments/network/TV (last access: 19 November 2021),
2008. a
Istituto Nazionale di Geofisica e Vulcanologia (INGV): Seismic Emergency
for Ischia by Sismiko (2017–2021), International Federation of Digital Seismograph Networks [data set], available at:
https://www.fdsn.org/networks/detail/ZM_2017/ (last access: 19 November 2021), 2017. a
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Istituto di Geologia
Ambientale e Geoingegneria (CNR-IGAG), Istituto per la Dinamica dei
Processi Ambientali (CNR-IDPA), Istituto di Metodologie per l'Analisi
Ambientale (CNR-IMAA), and Agenzia Nazionale per le nuove tecnologie,
l’energia e lo sviluppo economico sostenibile (ENEA): Rete del Centro di
Microzonazione Sismica (CentroMZ), sequenza sismica del 2016 in
Italia Centrale, Instituto Nazionale di Geofisica e Vulcanologia [data set], https://doi.org/10.13127/SD/KU7XM12YY9, 2018. a
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS):
North-East Italy Seismic Network, International Federation of Digital Seismograph Networks [data set], https://doi.org/10.7914/SN/OX, 2016. a
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.,
Guadarrama, S., and Darrell, T.: Caffe: Convolutional Architecture for
Fast Feature Embedding, arXiv [preprint], arXiv:1408.5093, 20 June 2014. a
Johnson, P. A., Rouet Leduc, B., Pyrak-Nolte, L. J., Beroza, G. C., Marone,
C. J., Hulbert, C., Howard, A., Singer, P., Gordeev, D., Karaflos, D.,
Levinson, C. J., Pfeiffer, P., Puk, K. M., and Reade, W.: Laboratory
earthquake forecasting: A machine learning competition, P.
Natl. Acad. Sci. USA, 118, e2011362118,
https://doi.org/10.1073/pnas.2011362118, 2021. a
Jozinović, D., Lomax, A., Štajduhar, I., and Michelini, A.: Rapid prediction
of earthquake ground shaking intensity using raw waveform data and a
convolutional neural network, Geophys. J. Int., 222,
1379–1389, https://doi.org/10.1093/gji/ggaa233, 2020. a, b, c
Jozinović, D., Lomax, A. J., Stajduhar, I., and Michelini, A.: SSA 2021
Annual Meeting, Seismol. Res. Lett., 92, 1213–1479,
https://doi.org/10.1785/0220210025, 2021. a
Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., Meade, B. J., and
Gerstoft, P.: Machine Learning in Seismology: Turning Data into
Insights, Seismol. Res. Lett., 90, 3–14,
https://doi.org/10.1785/0220180259, 2018. a
Korneev, V. A., Nadeau, R. M., and McEvilly, T. V.: Seismological Studies at
Parkfield IX: Fault-Zone Imaging Using Guided Wave
Attenuation, B. Seismol. Soc. Am., 93,
1415–1426, https://doi.org/10.1785/0120020114, 2003. a
Kriegerowski, M., Petersen, G. M., Vasyura Bathke, H., and Ohrnberger, M.: A
Deep Convolutional Neural Network for Localization of Clustered
Earthquakes Based on Multistation Full Waveforms, Seismol.
Res. Lett., 90, 510–516, https://doi.org/10.1785/0220180320, 2018. a
Krischer, L., Megies, T., Barsch, R., Beyreuther, M., Lecocq, T., Caudron, C.,
and Wassermann, J.: ObsPy: a bridge for seismology into the scientific
Python ecosystem, Computational Science & Discovery, 8, 014003,
https://doi.org/10.1088/1749-4699/8/1/014003, 2015. a, b
Lanzano, G., Luzi, L., Russo, E., Felicetta, C., D'Amico, M., Sgobba, S., and
Pacor, F.: Engineering Strong Motion Database (ESM) flatfile [data
set], Tech. Rep., Istituto Nazionale di Geofisica e Vulcanologia (INGV),
https://doi.org/10.13127/esm/flatfile.1.0, 2018. a
Linville, L., Pankow, K., and Draelos, T.: Deep Learning Models Augment
Analyst Decisions for Event Discrimination, Geophys. Res.
Lett., 46, 3643–3651, https://doi.org/10.1029/2018GL081119, 2019. a
Lomax, A., Michelini, A., and Jozinović, D.: An Investigation of Rapid
Earthquake Characterization Using Single‐Station Waveforms and
a Convolutional Neural Network, Seismol. Res. Lett., 90,
517–529, https://doi.org/10.1785/0220180311, 2019. a
Magrini, F., Jozinovic, D., Cammarano, F., Michelini, A., and Boschi, L.: Local
earthquakes detection: A benchmark dataset of 3-component seismograms built
on a global scale, Artificial Intelligence in Geosciences, 1, 1–10,
https://doi.org/10.1016/j.aiig.2020.04.001, 2020. a, b
Margheriti, L., Nostro, C., Cocina, O., Castellano, M., Moretti, M., Lauciani,
V., Quintiliani, M., Bono, A., Mele, F. M., Pintore, S., Montalto, P.,
Peluso, R., Scarpato, G., Rao, S., Alparone, S., Di Prima, S., Orazi, M.,
Piersanti, A., Cecere, G., Cattaneo, M., Vicari, A., Sepe, V., Bignami, C.,
Valoroso, L., Aliotta, M., Azzarone, A., Baccheschi, P., Benincasa, A.,
Bernardi, F., Carluccio, I., Casarotti, E., Cassisi, C., Castello, B.,
Cirilli, F., D'Agostino, M., D'Ambrosio, C., Danecek, P., Cesare, W. D.,
Bina, E. D., Di Filippo, A., Di Stefano, R., Faenza, L., Falco, L., Fares,
M., Ficeli, P., Latorre, D., Lorenzino, M. C., Mandiello, A., Marchetti, A.,
Mazza, S., Michelini, A., Nardi, A., Pastori, M., Pignone, M., Prestifilippo,
M., Ricciolino, P., Sensale, G., Scognamiglio, L., Selvaggi, G., Torrisi, O.,
Zanolin, F., Amato, A., Bianco, F., Branca, S., Privitera, E., and Stramondo,
S.: Seismic Surveillance and Earthquake Monitoring in Italy,
Seismol. Res. Lett., 92, 1659–1671, https://doi.org/10.1785/0220200380, 2021. a
McKinney, W.: Data Structures for Statistical Computing in Python, in:
Proceedings of the 9th Python in Science Conference, edited by: van der Walt, S. and Millman, J., pp. 56–61, https://doi.org/10.25080/Majora-92bf1922-00a,
2010. a
McNamara, D. E. and Buland, R. P.: Ambient Noise Levels in the
Continental United States, B. Seismol. Soc.
Am., 94, 1517–1527, https://doi.org/10.1785/012003001, 2004. a
MedNet Project Partner Institutions: Mediterranean Very Broadband
Seismographic Network (MedNet), Instituto Nazionale di Geofisica e Vulcanologia [data set], https://doi.org/10.13127/SD/FBBBTDTD6Q, 1988. a
Megies, T., Beyreuther, M., Barsch, R., Krischer, L., and Wassermann, J.:
ObsPy – What can it do for data centers and observatories?, Ann. Geophys.-Italy, 54, 47–58, https://doi.org/10.4401/ag-4838, 2011. a, b
Meier, M.-A., Ross, Z. E., Ramachandran, A., Balakrishna, A., Nair, S.,
Kundzicz, P., Li, Z., Andrews, J., Hauksson, E., and Yue, Y.: Reliable
Real-Time Seismic Signal/Noise Discrimination With Machine
Learning, J. Geophys. Res.-Sol. Ea., 124, 788–800,
https://doi.org/10.1029/2018JB016661, 2019. a
Mele, F., Arcoraci, L., Battelli, P., Berardi, M., Castellano, C., Lozzi, G.,
Marchetti, A., Nardi, A., Pirro, M., and Rossi, A.: Bollettino Sismico
Italiano 2008 (Italian Seismic Bulletin 2008), Quaderni di geofisica, p. 45, available at:
https://istituto.ingv.it/images/collane-editoriali/quaderni-di-geofisica/quaderni-di-geofisica-2010/quaderno85.pdf (last access: 19 November 2021),
2010. a, b, c
Michelini, A., Margheriti, L., Cattaneo, M., Cecere, G., D'Anna, G., Delladio, A., Moretti, M., Pintore, S., Amato, A., Basili, A., Bono, A., Casale, P., Danecek, P., Demartin, M., Faenza, L., Lauciani, V., Mandiello, A. G., Marchetti, A., Marcocci, C., Mazza, S., Mele, F. M., Nardi, A., Nostro, C., Pignone, M., Quintiliani, M., Rao, S., Scognamiglio, L., and Selvaggi, G.: The Italian National Seismic Network and the earthquake and tsunami monitoring and surveillance systems, Adv. Geosci., 43, 31–38, https://doi.org/10.5194/adgeo-43-31-2016, 2016. a, b
Michelini, A., Faenza, L., Lanzano, G., Lauciani, V., Jozinović, D., Puglia,
R., and Luzi, L.: The New ShakeMap in Italy: Progress and Advances
in the Last 10 Yr, Seismol. Res. Lett., 91, 317–333,
https://doi.org/10.1785/0220190130, 2019. a
Michelini, A., Cianetti, S., Gaviano, S., Giunchi, C., Jozinović, D., and
Lauciani, V.: INSTANCE The Italian Seismic Dataset For Machine
Learning, INGV [data set], https://doi.org/10.13127/instance, 2021. a, b
Moretti, M., De Gori, P., Govoni, A., Margheriti, L., Piccinini, D., Pintore,
S., and Valoroso, L.: Seismic Data acquired by the SISMIKO Emergency
Group – Molise-Italy 2018 – T14, EIDA [data set], https://doi.org/10.13127/SD/FIR72CHYWU, 2018. a
Mousavi, S. M. and Beroza, G. C.: A Machine‐Learning Approach for
Earthquake Magnitude Estimation, Geophys. Res. Lett., 47,
e2019GL085976, https://doi.org/10.1029/2019GL085976, 2020. a, b
Münchmeyer, J., Bindi, D., Leser, U., and Tilmann, F.: The transformer
earthquake alerting model: A new versatile approach to earthquake early
warning, Geophys. J. Int., 225, 646–656, https://doi.org/10.1093/gji/ggaa609, 2020. a
Münchmeyer, J., Bindi, D., Leser, U., and Tilmann, F.: Earthquake magnitude
and location estimation from real time seismic waveforms with a transformer
network, Geophys. J. Int., 226, 1086–1104, https://doi.org/10.1093/gji/ggab139, 2021. a
National Observatory of Athens, Institute of Geodynamics, Athens: National
Observatory of Athens Seismic Network, International Federation of Digitial Seismograph Networks [data set], https://doi.org/10.7914/SN/HL, 1997. a
OGS (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale) and
University of Trieste: North-East Italy Broadband Network, International Federation of Digitial Seismograph Networks [data set],
https://doi.org/10.7914/SN/NI, 2002. a
Otović, E., Njirjak, M., Jozinović, D., Mauša, G., Michelini, A., and Štajduhar, I.: Intra-domain and cross-domain transfer learning for time series, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12142, https://doi.org/10.5194/egusphere-egu21-12142, 2021. a
Parolai, S.: Denoising of Seismograms Using the S Transform, B. Seismol. Soc. Am., 99, 226–234,
https://doi.org/10.1785/0120080001, 2009. a
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen,
T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E.,
DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L.,
Bai, J., and Chintala, S.: PyTorch: An Imperative Style,
High-Performance Deep Learning Library, in: Advances in Neural
Information Processing Systems 32, edited by: Wallach, H., Larochelle,
H., Beygelzimer, A., Alché-Buc, F. d., Fox, E., and Garnett, R., Curran Associates, Inc., 8024–8035, available at:
http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (last access: 19 November 2021),
2019. a
Perol, T., Gharbi, M., and Denolle, M.: Convolutional neural network for
earthquake detection and location, Sci. Adv., 4, e1700578,
https://doi.org/10.1126/sciadv.1700578, 2018. a
Quinteros, J., Carter, J. A., Schaeffer, J., Trabant, C., and Pedersen, H. A.:
Exploring Approaches for Large Data in Seismology: User and Data
Repository Perspectives, Seismol. Res. Lett., 92, 1531–1540,
https://doi.org/10.1785/0220200390, 2021. a
The pandas development team: pandas-dev/pandas: Pandas 1.1.4 (v1.1.4), Zenodo [code], https://doi.org/10.5281/zenodo.4161697, 2020. a
Ross, Z. E., Meier, M.-A., and Hauksson, E.: P Wave Arrival Picking and
First-Motion Polarity Determination With Deep Learning, J. Geophys. Res.-Sol. Ea., 123, 5120–5129,
https://doi.org/10.1029/2017JB015251,
2018b. a, b, c, d
Scognamiglio, L., Tinti, E., and Michelini, A.: Real-Time Determination of
Seismic Moment Tensor for the Italian Region, B.
Seismol. Soc. Am., 99, 2223–2242, https://doi.org/10.1785/0120080104,
2009. a
Seydoux, L., Balestriero, R., Poli, P., de Hoop, M., Campillo, M., and
Baraniuk, R.: Clustering earthquake signals and background noises in
continuous seismic data with unsupervised deep learning, Nat.
Commun., 11, 3972, https://doi.org/10.1038/s41467-020-17841-x, 2020. a
Shearer, P. M.: Imaging global body wave phases by stacking long-period
seismograms, J. Geophys. Res.-Sol. Ea., 96,
20353–20364, https://doi.org/10.1029/91JB00421, 1991. a
Strollo, A., Cambaz, D., Clinton, J., Danecek, P., Evangelidis, C. P.,
Marmureanu, A., Ottemöller, L., Pedersen, H., Sleeman, R., Stammler, K.,
Armbruster, D., Bienkowski, J., Boukouras, K., Evans, P. L., Fares, M.,
Neagoe, C., Heimers, S., Heinloo, A., Hoffmann, M., Kaestli, P., Lauciani,
V., Michalek, J., Odon Muhire, E., Ozer, M., Palangeanu, L., Pardo, C.,
Quinteros, J., Quintiliani, M., Antonio Jara-Salvador, J., Schaeffer, J.,
Schloemer, A., and Triantafyllis, N.: EIDA: The European Integrated
Data Archive and Service Infrastructure within ORFEUS,
Seismol. Res. Lett., 92, 1788–1795, https://doi.org/10.1785/0220200413, 2021. a
Trugman, D. T. and Shearer, P. M.: Strong Correlation between Stress Drop
and Peak Ground Acceleration for Recent M 1–4 Earthquakes in
the San Francisco Bay Area, B. Seismol. Soc.
Am., 108, 929–945, https://doi.org/10.1785/0120170245, 2018. a
Universita della Basilicata: UniBAS, International Federation of Digital Seismograph Networks [data set], available at:
https://www.fdsn.org/networks/detail/BA/ (last access: 19 November 2021), 2005. a
Universita Federico II Napoli: Irpinia Seismic Network, International Federation of Digital Seismograph Networks [data set], available at:
https://www.fdsn.org/networks/detail/IX/ (last access: 19 November 2021), 2005. a
University of Bari “Aldo Moro”: OTRIONS, International Federation of Digital Seismograph Networks [data set], https://doi.org/10.7914/SN/OT, 2013. a
University of Genoa: Regional Seismic Network of North Western
Italy, International Federation of Digital Seismograph Networks [data set], https://doi.org/10.7914/SN/GU, 1967. a
University of Trieste: Friuli Venezia Giulia Accelerometric Network, International Federation of Digital Seismograph Networks [data set],
https://doi.org/10.7914/SN/RF, 1993. a
Vallée, M., Ampuero, J. P., Juhel, K., Bernard, P., Montagner, J.-P., and
Barsuglia, M.: Observations and modeling of the elastogravity signals
preceding direct seismic waves, Science, 358, 1164–1168,
https://doi.org/10.1126/science.aao0746, 2017. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng,
Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors:
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Walter, J. I., Ogwari, P., Thiel, A., Ferrer, F., and Woelfel, I.: easyQuake:
Putting Machine Learning to Work for Your Regional Seismic
Network or Local Earthquake Study, Seismol. Res. Lett.,
92, 555–563, https://doi.org/10.1785/0220200226, 2020. a
Waskom, M. L.: seaborn: statistical data visualization, Journal of Open Source
Software, 6, 3021, https://doi.org/10.21105/joss.03021, 2021. a
Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J., and Soto, H.: SeisBench – A Toolbox for Machine Learning in Seismology, arXiv [preprint], arXiv:2111.00786, 1 November 2021. a
Yeck, W. L. and Patton, J.: Waveform Data and Metadata used to National
Earthquake Information Center Deep-Learning Models, USGS [data set],
https://doi.org/10.5066/P9OHF4WL, 2020. a
Yeck, W. L., Patton, J. M., Ross, Z. E., Hayes, G. P., Guy, M. R., Ambruz,
N. B., Shelly, D. R., Benz, H. M., and Earle, P. S.: Leveraging Deep
Learning in Global 24/7 Real-Time Earthquake Monitoring at the
National Earthquake Information Center, Seismol. Res.
Lett., 92, 469–480, https://doi.org/10.1785/0220200178, 2020. a, b
Zentralanstalt fur Meterologie und Geodynamik (ZAMG): Province Sudtirol, International Federation of Digital Seismograph Networks [data set], available at:
https://www.fdsn.org/networks/detail/SI/ (last access: 19 November 2021), 2006. a
Zhang, J. and Chen, J.: Joint seismic traveltime and waveform inversion for
near surface imaging, in: SEG Technical Program Expanded Abstracts
2014, Society of Exploration Geophysicists, Denver, Colorado, pp. 934–937,
https://doi.org/10.1190/segam2014-1501.1, 2014. a
Zhang, X., Zhang, J., Yuan, C., Liu, S., Chen, Z., and Li, W.: Locating induced
earthquakes with a network of seismic stations in Oklahoma via a deep
learning method, Scientific Reports, 10, 1941,
https://doi.org/10.1038/s41598-020-58908-5, 2020. a
Zhu, L., Peng, Z., McClellan, J., Li, C., Yao, D., Li, Z., and Fang, L.: Deep
learning for seismic phase detection and picking in the aftershock zone of
2008 Mw 7.9 Wenchuan Earthquake, Phys. Earth Planet.
In., 293, 106261, https://doi.org/10.1016/j.pepi.2019.05.004,
2019.
a, b
Zhu, W., Mousavi, S. M., and Beroza, G. C.: Seismic Signal Denoising and
Decomposition Using Deep Neural Networks, IEEE T.
Geosci. Remote, 57, 9476–9488,
https://doi.org/10.1109/TGRS.2019.2926772, 2019. a, b, c
Short summary
We present a dataset consisting of seismic waveforms and associated metadata to be used primarily for seismologically oriented machine-learning (ML) studies. The dataset includes about 1.3 M three-component seismograms of fixed 120 s length, sampled at 100 Hz and recorded by more than 600 stations in Italy. The dataset is subdivided into seismograms deriving from earthquakes (~ 1.2 M) and from seismic noise (~ 130 000). The ~ 54 000 earthquakes range in magnitude from 0 to 6.5 from 2005 to 2020.
We present a dataset consisting of seismic waveforms and associated metadata to be used...
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