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The data publication contains a dataset for fast assessment of earthquakes and early warning based on seismic waveforms. The dataset encompasses Italy and surrounding refions. Due to the large scale of the dataset, it is intended for use in machine learning. A similar dataset for Japan, with the same specifications as the one provided in this data publications, can be obtained using the scripts at https://github.com/yetinam/TEAM
The data publication contains a dataset for fast assessment of earthquakes based on seismic waveforms. The dataset encompasses Northern Chile. Due to the large scale of the dataset, it is intended for use in machine learning. A similar dataset for chile has been published as Münchmeyer et al. (2020). A similar dataset for Japan can be obtained using the scripts at https://github.com/yetinam/TEAM The datasets are provided as a hdf5-file (Folk et al. 2011), a hierachical file format. Source code for reading and processing the data is available at https://github.com/yetinam/TEAM. The hdf5-file contains the two groups “metadata” and “data” that are described below. These groups are the hdf5-analog of folders in a file system.
The European-Mediterranean Seismological Centre (EMSC) is a non-profit scientific organization aiming at establishing and operating a rapid earthquake detection system globally and in particular in the European and Mediterranean regions as well as facilitating exchange between seismological institutes. The EMSC has been a pioneer in citizen seismology by collecting in-situ information on the earthquake impact directly from the witnesses. The EMSC has been collecting citizen intensity felt reports at a global scale for many years via two channels: its websites and its “LastQuake” smartphone application. These felt reports are collected through a set of 12 cartoons representing the 12 levels of the European Macroseismic Scale (Grünthal, 1998). They provide rapid information on how the earthquake’s impact is felt by the local population. The EMSC felt reports were shown to be consistent with the USGS Did You Feel It? (Wald et al., 2011) responses and with manually derived macroseismic datasets (Bossu et al., 2017). Such felt reports are provided for a set of 36 earthquakes, each tagged with a unique ID number. They are only considered for intensity values of up to 10, since higher values are unrealistic. Additionally, an interactive map of the aftershocks distribution is provided for each earthquake. These aftershocks are selected from the EMSC catalogue in the 14 days after the event and within 500km of the epicentre location. On each map, the beachball representing the two nodal planes as given by the Global Centroid Moment Tensor catalogue (Dziewonski et al., 1981; Ekstrom et al., 2012) is displayed at the epicentre location. For each event (identified by unique id number), the first line indicates catalogue information on the earthquake (event_id, region, origin_time (UTC), latitude, longitude, depth, magnitude, strike angle from GCMT) Each following line is a felt report gathered by the EMSC including, the longitude, latitude, reported intensity and report time.
TEAM, the Transformer Earthquake Alerting Model is a deep learning model for real time estimation of peak ground acceleration (TEAM), earthquake magnitude and earthquake location (TEAM-LM). This software package contains the joint implementation of both TEAM and the derivative TEAM-ML, as well as the scripts for training and evaluating these models. In addition, it contains scripts to download an early warning datasets for Japan and implementations of baseline approaches for the estimation of earthquake magnitude and peak ground acceleration. TEAM is implemented in Python. TEAM and TEAM-ML have a variety of configuration parameters that are documented in the README. These configurations need to be provided in JSON format. In addition, multiple example configuration files are provided in the subdirectories pga_configs and magloc_configs. Please note that this implementation is intended for research purpose only. Production use is discouraged.
We present a Python application to download events and data from FDSN webservices (https://www.fdsn.org/webservices/) and compute the events energy Magnitude (Me), producing outputs in several formats (QuakeML, HDF, CSV, HTML). This software has been used to compile a seismic catalogue including Me estimated form P-waves recorded at teleseismic distances in the range 20° ≤ ∆ ≤ 98°, available at GFZ Data Services (Bindi et al., 2023; https://doi.org/10.5880/GFZ.2.6.2023.010). The software complete pipeline (download and energy magnitude computation) can be deployed locally via terminal commands or chained and scheduled on a server to compute the energy magnitude in semi-realtime (e.g. daily or weekly).
The dataset is supplementary material to the Solid Earth research article of Leonhardt et al. (2021). The dataset is a high-resolution catalog of seismicity framing the stimulation campaign of a 6.1 km deep Enhanced Geothermal System (EGS) in Helsinki suburban area, Finland. Within the St1 Deep Heat project, a total of 18,160 m3 of fresh water was injected into crystalline rocks during 49 days in summer 2018. The seismicity was monitored by a 12-level seismometer array at >2km depth and a seismic network of near-surface borehole sensors surrounding the EGS site. We expanded and refined the original catalog of Kwiatek et al. (2019) including detected seismic events and earthquakes that occurred two month after the end of injection and determining new locations and relocations on the basis of a new velocity model derived from a post-stimulation vertical seismic profiling campaign. A detailed description of the catalog reprocessing as well as a description of basic statistical and spatio-temporal properties of the catalog can be find in the data description file. Definition of columns in the data table (also in the header of the data): event ID, event class, datenumber [integer part = day since year 0], year, month, day, hour, minute, seconds, local magnitude MLHEL, moment magnitude MW, absolute location in local cartesian coordinates [easting (m), northing (m), altitude (m)], relocated location in local cartesian coordinates [easting (m), northing (m), altitude (m)], fault plane solutions of estimated focal mechnisms [strike (°), dip (°), rake(°)] and root mean square fault plane uncertainty of estimated focal mechanisms.
The dataset is supplementary material to Kwiatek et al. (2019, Science Advances).The dataset is a refined seismic catalog acquired during the hydraulic stimulation of the future geothermal sites located in Espoo, Finland. There, the injection well, OTN-3, was drilled down to 6.1 km-depth into Precambrian crystalline rocks. Well OTN-3 was deviated 45° from vertical and an open hole section at the bottom was divided into several injection intervals. A total of 18,159 m3 of fresh water was pumped into crystal-line rocks during 49 days in June- and July, 2018. The stimulation was monitored in near-real time using (1) a 12-level seismometer array at 2.20-2.65 km depth in an observation well located ~10 m from OTN3 and (2) a 12-station network installed in 0.3-1.15 km deep bore-holes surrounding the project site. On completion of stimulation it the catalog contained 8452 event detections overall, and 6152 confirmed earthquakes located in the vicinity of the project site (epicentral distance from the well head of OTN-3 <5 km). These were recorded in a time period lasting 59 days: 49 days of active stimulation campaign and the 10 days following completion.The initial industrial seismic catalog of 6150 earthquakes was manually reprocessed. The P- and S-wave arrivals of larger seismic events with M>0.5 were all manually verified, and, if necessary, refined. Earthquakes with sufficient number of phases and seemingly anomalous hypocenter depths (e.g. very shallow or very deep) were manually revised as well. The hypocenter locations were calculated using the Equivalent differential time method and optimized with an Adaptive Simulated Annealing algorithm. The updated catalog contained 4,580 earthquakes that occurred at hypocenter depths 4.5-7.0 km, in the vicinity of the stimulation section of OTN-3. To increase the precision of their locations, the selected 2155 earthquakes with at least 10 P-wave and 4 S-wave picks were relocated using the double-difference relocation technique. The relocation uncertainties were estimated using bootstrap resampling technique. The relocation reduced the relative precision of hypocenter determination to approx. 66 m and 27 m for 95% and 68% of relocated earthquakes. The final relocated catalog that constitutes the here published contained 1,977 earthquakes (91% of the originally selected events).
This data publication includes a grid composed by contiguous 25 x 25 km square elements covering the Italian area and each parametrized by 1) the maximum length of faults included within the cell, 2) the maximum magnitude from instrumental seismic data, 3) the maximum magnitude from historical seismic data, 4) the maximum magnitude calculated from fault length using empirical scaling laws.This collection represents the basis to a work (Trippetta et al., 2019) aiming to test a fast method comparing the geologic (faults) and the seismologic (historical-instrumental seismicity) information available for a specific region. To do so, (1) a comprehensive catalogue of all known faults and (2) a comprehensive catalogue of earthquakes were compiled by merging the most complete available databases; (3) the related possible maximum magnitudes were derived from fault dimensions, upon the assumption of seismic reactivability of any fault; (4) the calculated magnitudes were compared with earthquake magnitudes recorded in historical and instrumental time series.Faults: to build the dataset of faults for Italy, the following databases were merged: (1) the entire faults collection after the Italian geological maps at the 1:100,000 scale (available online at www.isprambiente.it); (2) the faults compilation from the structural model of Italy at the 1:500,000 scale (Bigi et al., 1989); (3) faults provided in the ITHACA-Italian catalogue of capable faults (Michetti et al., 2000); and (4) the inventory of active faults of the GNDT (Gruppo Nazionale per la Difesa dai Terremoti, Galadini et al., 2000). To improve and implement the database, published complementary studies were selected for some specific areas considered to not be exhaustively covered by the aforementioned collection of faults, including Sardinia, SW Alps, Tuscany, the Adriatic front, Puglia, and the Calabrian Arc. For these areas, faults were selected on the grounds of scientific contributions that documented recent fault activity based on seismic, field, and paleoseismological data. In particular, for the southern Sardinia, the fault pattern proposed by Casula et al. (2001) was used. For the SW Alps, the works of Augliera et al. (1994), Courboulex et al. (1998), Larroque et al. (2001), Christophe et al. (2012), Sue et al. (2007), Capponi et al. (2009), Turino et al. (2009) and Sanchez et al. (2010) were followed. For the Tuscany area, Brogi et al. (2003), Brogi et al. (2005), Brogi (2006), Brogi (2008), Brogi (2011), and Brogi and Fabbrini (2009) were consulted. For the buried northern Apennines and Adriatic front, the fault datasets provided by Scrocca (2006), Cuffaro et al. (2010), and Fantoni and Franciosi (2010) were used. For the Puglia region, data from Patacca and Scandone (2004) and Del Gaudio et al. (2007) were used, while for the Calabrian Arc data were obtained from Polonia et al. (2016).Seismicity: to obtain a complete earthquake catalogue for the Italian territory, the following catalogues of instrumental and historical seismicity were integrated: (1) the CSI1.1 database (http://csi.rm.ingv.it; Castello et al., 2006) for the period 1981–2002, (2) the ISIDe database (http://iside.rm.ingv.it/iside/; IsideWorkingGroup, 2016) for the period 2003–2017 (Figure 3) and the CPTI15 (https://emidius.mi.ingv.it/CPTI15-DBMI15/; Rovida et al., 2016) for the period 1000-1981.The CSI 1.1 database (Castello et al., 2006) is a relocated catalogue of Italian earthquakes during the period 1997–2002. This collection derives from the work of Chiarabba et al. (2005). Most seismic events are lower than 4.0 in magnitude and are mostly located in the upper 12 km of the crust. A few earthquakes exceed magnitude 5.0, and the largest event is Mw 6.0. Due to their poorly constrained location, events with Mw < 2.0 were removed.The ISIDe database (IsideWorkingGroup, 2016) provides the parameters of earthquakes obtained by integrating data from real time and Italian Seismic Bulletin earthquakes. The time-span of this compilation begins in 1985. To avoid an overlap with the CSI database, only the time interval 2003–2017 was considered. Mw = 2.0 is the lower limit used for earthquake magnitude. The CPTI15 database integrates the italian macroseismic database version 2015 (DBMI15, Locati et al., 2016) and instrumental data from 26 different catalogues, databases and regional studies starting from the 1000 up to the 2014. To avoid overlapping of data with the utilized instrumental datasets, from the CPTI2015 we took data for the period 1000-1981 in the range of Mw 4-7.Method: starting from the entire faults dataset, the length of each structure was calculated (Lf, in km). Then, the Italian territory was divided into a grid with square cells of 25 x 25 km. The length of the longest fault crossing each cell characterizes the parameter “fault length” (Lf) of the considered cell. In the second step, these lengths were used as the input parameter to empirically derive the magnitude. The equations provided by Leonard (2010), were applied for earthquake magnitude-fault length relationships to infer the Potential Expected Maximum Magnitude as M = a + b ∗ log (Lf), with a=4.24 and b=1.67. The obtained magnitudes were assigned to each single cell. Furthermore, the maximum magnitude recorded/reported in instrumental/historical catalogs is associated to each containing cell.The resulting datasets are presented in txt format and included in the following files:- Grid_Coordinates.txt (contains ID and coordinates of grid's elements)- Grid_Structure.txt (contains geometry and specifications of the used grid)- Table_results (five columns table containing 1=element ID, 2= element max fault length (Lf_max in km), 3=element max Mw from instrumental record (MwInstr_max), 4=element max Mw from historical record (MwHist_max), 5=element max Mw derived by empirical relationship (PEMM).- The full list of references is included in the file Petricca_2018-003_References.txt
In Bindi et al. (2019) a harmonized local magnitude scale across Europe has been derived using data disseminated by network operators through the European Integrated Data Archive (EIDA). This data set contains regionalized non-parametric attenuation tables, attenuation corrections to the parametric model and station corrections for both non-parametric and parametric models for more than 2000 stations in Europe. Regionalization has been performed considering six different regions covering Europe and the polygons defining them are also provided. Data are subject to updates that can be triggered by the availability of new and substantial input data (reviewed earthquake catalogues and/or new waveforms). Each update will be released with a new version of the data. The data are provided in ASCII format (.csv).
This catalogue is the extended version of “The European-Mediterranean Earthquake Catalogue (EMEC) for the last millennium” (Grünthal and Wahlstrom, 2012, 2012a). It is an earthquake catalogue for tectonic events in the broader European Mediterranean area. It reached from the Azores (Mid-Atlantic Ridge) in the west, to Africa north of the Sahara in the south, the Arctic Sea in the north, and the regions of Levant, eastern Turkey, and the Caucasus in the west. This areal coverage gave the name to the catalogue: EMEC—The European-Mediterranean Earthquake Catalogue. It extends the previous version (Grünsthal and Wahlström, 2012), by the years 2007 to 2021 and thus contains tectonic events for the period AD 1000 to 2021 with a uniform magnitude Mw from the threshold of 3.5. The dataset contains 71271 entries.
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