2026-10-11T04:43:23Z https://oai.datacite.org/oai

doi:10.13127/ai/aquila2009 2025-04-16T16:20:17Z WNGN CRUI.INGV

2 CRUI.INGV 10.13127/AI/AQUILA2009 Bagagli, Matteo Matteo Bagagli https://orcid.org/0000-0001-9409-8667 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Valoroso, Luisa Luisa Valoroso https://orcid.org/0000-0001-5014-0958 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Michelini, Alberto Alberto Michelini https://orcid.org/0000-0001-6716-8551 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Cianetti, Spina Spina Cianetti https://orcid.org/0000-0002-0690-7274 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Gaviano, Sonja Sonja Gaviano https://orcid.org/0000-0003-3481-2923 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Giunchi, Carlo Carlo Giunchi https://orcid.org/0000-0002-0174-324X Istituto Nazionale di Geofisica e Vulcanologia (INGV) Jozinović, Dario Dario Jozinović https://orcid.org/0000-0001-7443-3915 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Lauciani, Valentino Valentino Lauciani https://orcid.org/0000-0002-9672-7428 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Istituto Nazionale di Geofisica e Vulcanologia (INGV) 2023 seismic waveforms and associated metadata machine learning big data seismic event detection ground motion estimation analysis benchmarking seismic tomography Bagagli, Matteo Matteo Bagagli https://orcid.org/0000-0001-9409-8667 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Giunchi, Carlo Carlo Giunchi https://orcid.org/0000-0002-0174-324X Istituto Nazionale di Geofisica e Vulcanologia (INGV) Chiaraluce, Lauro Lauro Chiaraluce https://orcid.org/0000-0002-9697-6504 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Di Stefano, Raffaele Raffaele Di Stefano https://orcid.org/0000-0003-3489-7453 Istituto Nazionale di Geofisica e Vulcanologia (INGV) Piccinini, Davide Davide Piccinini https://orcid.org/0000-0002-1826-646X Istituto Nazionale di Geofisica e Vulcanologia (INGV) Schaff, David P. David P. Schaff https://orcid.org/0000-0003-2580-9546 Lamont-Doherty Earth Observatory Waldhauser, Felix Felix Waldhauser https://orcid.org/0000-0002-1286-9737 Columbia University Istituto Nazionale di Geofisica e Vulcanologia (INGV) https://ror.org/00qps9a02 2009-04-06/2009-12-20 2023-11 en 10.1002/jgrb.50130 https://eida.ingv.it 10.13127/iside 10.13127/sd/x0fxnh7qfy 10.13127/sd/fbbbtdtd6q 10.15778/resif.xj2009 10.13127/instance 125 GB text/csv application/x-hdf5 Open Access Creative Commons Attribution 4.0 International The 2009 L'Aquila earthquake, with a moment magnitude (Mw) of 6.1, was a highly impactful and damaging seismic event in the South-Central Apennines region. This study presents an extensive analysis of the aftershock sequence spanning 254 days from April 6th to December 20th 2009, thus providing a valuable asset and allowing for unprecedented insight analysis in aftershocks characterization with Machine Learning (ML) approaches. The dataset has been conveniently formatted for a variety of machine learning applications and frameworks in computer science, such as Seisbench. It employs the widely used Hierarchical Data Format (HDF5) for storing and organizing large waveform data, as well as classic CSV files for managing associated metadata. To simplify accessibility and provide greater flexibility to end-users, the dataset has been divided in a day-wise manner using the DOY nomenclature. This approach allows for easy data retrieval and utilization, allowing more freedom in its usage for the end-user. We packaged a comprehensive dataset comprising 63,704 earthquakes, resulting in over 1.25 million 3-component seismic traces. The dataset includes absolute (HypoEllipse) and relative (HypoDD) earthquake locations, derived from data collected by three seismic networks (MN, IV, XJ) encompassing 67 stations. We distribute 3-component waveforms (70 seconds long) sorted in an event-station manner and trimmed around to the associated first-arrival phase. A key aspect of this study is the collection of multiple P and S wave pick arrivals within the same cut (up to 8 total picks per trace), which significantly enhances the potential for current ML algorithms development, especially concerning seismic-phase picking. We collect and provide 201 metadata fields offering detailed information on stations, traces, sources, paths, and data quality. The data encompasses the seismic events count and velocity information, enabling a thorough understanding of the aftershock dynamics. This dataset aims to improve ML algorithms in seismology and advance earthquake detection, characterization, hazard assessment, and understanding of seismic processes. In a nutshell: - 254 days of aftershocks (2009-04-06 up to 2009-12-20). - 63,704 earthquakes - 1,258,006 3-component traces cut of 70 seconds - Absolute (HypoEllipse) and relative (HypoDD) locations. - 3 seismic networks (MN, IV, XJ) for a total of 67 stations. - Multiple P and S pick arrivals (up to 8 per cut) that allow improvements in current Machine Learning algorithms development and analysis. - 201 metadata providing information on the station, trace, source, path, and data quality. - Metadata information on both counts and velocity. Italy Italy 13 13.7 42.1 42.7 Ministero dell'Università e della Ricerca (MUR) https://doi.org/10.13039/501100003407 Pianeta Dinamico: Geoscienze per la comprensione dei meccanismi di funzionamento della Terra e dei conseguenti rischi naturali