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This repository contains the site amplification functions obtained by Bindi et al. (2023). The site amplifications were obtained through a Generalized Inversion Technique (GIT) applied to seismic recordings downloaded from EIDA (Strollo et al., 2021) and EarthScope (https://service.iris.edu/) using stream2segment software (Zaccarelli, 2018). We computed the Fourier spectra of S-waves windows considering the square root of the sum of the two horizontal components squared. The site amplifications are relative to the station CH.LSS (station Linth-Limmern of the Swiss network, https://stations.seismo.ethz.ch/en/station-information/station-details/station-given-networkcode-and-stationcode/index.html?networkcode=CH&stationcode=LLS), installed on rock with shear wave velocity averaged over the top 30 m equal to vs30=2925 m/s (Fäh et al. 2009). The site amplification at the reference station LLS is constrained to be equal to 1 for frequencies f below 10 Hz and to the function exp[−0.015π(f−10)] above 10 Hz, to account for near-surface attenuation effects at high frequencies. Details about the decomposition can be found in Bindi et al (2023). The file siteAmp_repo.csv uses as field separator the semicolon (;). It contains: - column freq: values of frequency between 0.5 and 20 Hz; - columns with site amplifications: 3001 columns with column name given by network_station_channel (e.g. GR_MOX_HH indicated station MOX of network GR, channel HH). The R script (R Core Team, 2024) plotRepo.R shows how to read and plot the site amplification for a given station.
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The interactive web page contains supplementary information for a publication by Hensch et al. 2019: "Deep low-frequency earthquakes reveal ongoing magmatic recharge beneath Laacher See Volcano (Eifel, Germany)". Details on the analysis of three tectonic and nine deep low-frequency earthquakes are given, including parameter results, error estimates, and figures. The analysis has been performed using the Grond software package (Heimann et. al 2018).The open source software for seismic source parameter optimization (Grond, Heimann et al., 2018) implements a bootstrap-based method to retrieve solution sub-spaces, parameter trade-offs and uncertainties of earthquake source parameters. Synthetic and observed P and S phase waveforms are restituted to displacement and filtered between 0.5 and 5 Hz in variable frequency ranges, depending on the signal-to-noise ratio (SNR) and the character of the signals. Station amplification factors and transfer functions have been evaluated before the restitution using an empirical calibration method (see Dahm et al., 2018). From waveforms, different types of body wave attributes were calculated, as amplitude spectra, envelopes, and amplitude spectral ratios.The Green's functions (GF) were calculated with the orthonormal propagator method (QSEIS, Wang, 1999; see https://github.com/pyrocko/fomosto-qseis/), for a 1 km grid spacing in a volume of 150 km source-receiver distances and 1 - 50 km source depths. The sampling rate was 40 Hz and the GF include near field terms. All GF are stored in a Pyrocko GF store (Pyrocko toolbox, see Heimann et al., 2017). We use a nearest neighbor interpolation in between the grid points of the pre-computed GF.Restituted observed and synthetic ground displacement time series are filtered and windowed between [-2 s; +3 s] from the expected phase arrival, given the tested candidate source model at each forward modeling step in the optimization. Additional to full waveforms, amplitude spectra, envelopes and spectral ratios between P-SV and SH-SV waves are compared. For spectral ratios, a water level approach was implemented to avoid bias from high noise. All components of the mixed inversion received a proper linear weighting with factors between 0.5 and 3, which was selected after running tests with some master events. Weighting and frequency range were defined different for earthquakes with magnitudes above or below ML 2. P and S phase arrivals have been picked to ensure correct selection of time windows during the centroid inversion, and station blacklists were considered event-wise, depending on the SNR.The plots show for every event the data fits and different types of solution plots. The naming of pages is self-explanatory, but more information can be found in the Grond documentation (https://pyrocko.org/grond/). In order to evaluate the ensembles of solutions for interpretation, we extended the standard statistical analysis of Grond to consider a cluster analysis of source mechanism distributions before statistical analysis. This is introduced because the best ensemble solutions of many of the DLF events show higher variability and groups of different mechanisms. A simple mean or median does not always represent the families of best performing solutions. We therefore declustered the ensemble of best solutions using the method of Cesca et al. (2013), applying the Kagan angle norm, and performed the statistical analysis for each individual cluster.
The 'Earthquake Network’ (EQN) is an app which detects earthquakes by creating an ad-hoc network of smartphone's accelerometer sensors and provides early warnings of earthquakes via the same smartphone app. The EMSC (Euro-Mediterranean Seismological Centre) and the University of Bergamo conducted an online survey, following an earthquake of magnitude M8 on 2019-05-26 07:41:13.6 UTC in Northern Peru with epicentre (5.81S, 75.27W). This survey targeted EQN users in the felt area of the earthquakes and was conducted from 2019-07-23 to 2019-08-18. It aimed at assessing users’ understanding and reaction to the EQN early warning for this specific earthquake. The questionnaire was written in Spanish since it is the most commonly spoken language in the studied area. Individuals who use the app in Spanish were invited to complete the survey via an advertisement on the Earthquake Network app. A PDF containing the questionnaire and the relationship between the questions is included in this archive. 3805 respondents took the survey, including 2 719 that were actually in the area at the time. The analysis Results derived from this dataset will be included as part of a submitted Science article (Bossu et al. '“Shaking in 5 seconds!” A Voluntary Smartphone-based Earthquake Early Warning System', 2021) to show that respondents received notifications from the Earthquake Network App before feeling the shaking but also that many did not immediately “drop, hold and cover' and were too intent on warning those close to them of the impending danger. All respondents consented that their data could be used for research purposes. The EMSC and University of Bergamo made sure not to collect or diffuse personal data. The dataset is a zip-file that contains the questionnaire responses as a comma-separated text file (csv) and a pdf containing a representation of the questionnaire that was presented to respondents.
This data publication contains seismic waveform data of 507 earthquakes recorded during the St1 Deep Heat project in June and July 2018, where the 6.1 km deep OTN-3 well near Helsinki, Finland, was hydraulically stimulated over 49 days (Kwiatek et al., 2019). The waveforms were recorded on a surrounding seismic monitoring network consisting of 12 stations, deployed at epicentral distances between 0.6 to 8.2 km and at depths between 0.23 to 1.15 km. Each station consists of three-component, 4.5 Hz, Sunfull PSH geophones, sampling at 500 Hz. The 507 earthquakes analysed were chosen from the relocated event catalogue by Leonhardt et al. (2021a). The dataset is supplementary material to the Geophysical Research Letters research article of Holmgren et al. (2022), which applied the Empirical Green’s Function technique to examine microseismic rupture behaviour at the Helsinki site.
The Community Stress Drop Validation Study has been organized as a technical activity group (TAG) of SCEC (Southern California Earthquake Center) with the aim of investigating the source parameters of the 2019 Ridgecrest seismic sequence in California. Information about the stress drop TAG are available trough the benchmark web-page (https://www.scec.org/research/stress-drop-validation). Several groups applied different techniques to a shared data set with the objective of extracting source parameters (e.g. seismic moment and corner frequency) and in turn to estimate the stress drop. We applied a spectral decomposition approach known as generalized inversion technique (GIT) and the overall analyses are presented in a series of two articles (Bindi et al 2023a; Bindi et al 2023b). Results in the form of files, figures, and tables are disseminated through this archive.
Ground-motion flatfiles are commonly used to develop ground motion models (GMMs) and for systematical analysis of ground motions over a wide range of distances and earthquake magnitudes. A flatfile is organized as a table of properties and various intensity measures of earthquake waveforms, including data processing parameters. Here we present a comprehensive processed ground-motion flatfile containing data from the Kyoshin (K-NET) and Kiban-Kyoshin (KiK-net) networks operated by National Research Institute for Earth Science and Disaster Resilience (NIED) (2019) in Japan (Okada et al., 2004; Aoi et al., 2011). This flatfile contains 914,628 ground motions from 18,018 events recorded by 1,749 stations. Out of these, 434,898 ground-motions are from KiK-net and 479,730 from K-net. The events were recorded between June 1996 and September 2024, covering distances up to 1200 km and magnitudes between 2.5 and 9. The ground motions have been automatically processed, and metadata describing each event and record are provided in the flat file. An overview of the flatfiles and the processing steps to derive the reported ground-motion parameters is provided in this report. Further details and discussion about the flatfile compilation can be found in the corresponding publication: Loviknes, K., von Specht, S., Lilienkamp, H., Händel, A., and Cotton, F. (2025). Harmonized KiK-net and K-NET flatfile for systematic analysis of earthquake ground motions (submitted to Seismica, February 2025).
This archive disseminated through the GFZ-Data Service includes both results and information as-sociated to Bindi et al. (2023). In particular, the archive includes a seismic catalogue reporting ener-gy magnitude Me estimated form vertical P-waves recorded at teleseismic distances in the range 20°≤ D ≤ 98°, following Di Giacomo et al (2008, 2010). The catalogue is built considering 6349 earth-quakes included in the GEOFON (Quinteros et al, 2021) catalogue with moment magnitude Mw larger than 5 and occurring after 2011. Tools used to compute the energy magnitude are free available. In particular, we used stream2segment (Zaccarelli, 2018) to download data from IRIS (https://ds.iris.edu/ds) and EIDA (Strollo et al., 2021) repositories, and me-compute [Zaccarelli, 2023) to process waveforms and compute Me. The methodology applied to me-compute is also implemented as add-on for SeicomP (GFZ and Gempa, 2020) in order to allow the real time computation of Me (https://github.com/SeisComP/scmert).
We perform a teleseismic P-wave travel-time tomography to examine the geometry and structure of subducted lithosphere in the upper mantle beneath the Alpine orogen. The tomography is based on waveforms recorded at over 600 temporary and permanent broadband stations of the dense AlpArray Seismic Network deployed by 24 different European institutions in the greater Alpine region, reaching from the Massif Central to the Pannonian Basin and from the Po plain to the river Main. Teleseismic travel times and travel-time residuals of direct teleseismic P-waves from 331 teleseismic events of magnitude 5.5 and higher recorded between 2015 and 2019 by the AlpArray Seismic Network are extracted from the recorded waveforms using a combination of automatic picking, beamforming and cross-correlation. The resulting database contains over 162.000 highly accurate absolute P-wave travel times and travel-time residuals. For tomographic inversion, we define a model domain encompassing the entire Alpine region down to a depth of 600 km. Predictions of travel times are computed in a hybrid way applying a fast Tau-P method outside the model domain and continuing the wavefronts into the model domain using a fast marching method. We iteratively invert demeaned travel-time residuals for P-wave velocities in the model domain using a regular discretization with an average lateral spacing of about 25 km and a vertical spacing of 15 km. The inversion is regularized towards an initial model constructed from a 3D a priori model of the crust and uppermost mantle and a 1D standard earth model beneath. The resulting model provides a detailed image of slab configuration beneath the Alpine and Apenninic orogens. Major features are a partly overturned Adriatic slab beneath the Apennines reaching down to 400 km depth still attached in its northern part to the crust but exhibiting detachment towards the southeast. A fast anomaly beneath the western Alps indicates a short western Alpine slab whose easternmost end is located at about 100 km depth beneath the Penninic front. Further to the east and following the arcuate shape of the western Periadriatic Fault System, a deep-reaching coherent fast anomaly with complex internal stucture generally dipping to the SE down to about 400 km suggests a slab of European origin limited to the east by the Giudicarie fault in the upper 200 km but extending beyond this fault at greater depths. In its eastern part it is detached from overlying lithosphere. Further to the east, well-separated in the upper 200 km from the slab beneath central Alps but merging with it below, another deep-reaching, nearly vertically dipping high-velocity anomaly suggests the existence of a slab beneath the Eastern Alps of presumably the same origin which is completely detached from the orogenic root. The data are fully described in Paffrath et al. (2021). The model is provided as tabular data with six columns (1) Longitude (deg), (2) Latitude (deg), (3) Depth (km), (4) vp (km/s), (5) dVp (%), (6) Resolution.
The 'Earthquake Network’ (EQN) is an app which detects earthquakes by creating an ad-hoc network of smartphones' accelerometer sensors and provides early warnings for earthquakes via the same smartphone app. Detections are not due to individual smartphone measurements but due to near-simultaneous trigger signals from clusters of smartphones running the app. Therefore detections are normally located in the closest populated regions to an earthquake's epicentre. In order to investigate the mechanisms of EQN's earthquake detection system, we searched for seismic accelerometer stations with publically available data that were close to the EQN detection locations (rather than close to the epicentre). This confirmed that EQN's detections followed strong shaking motions but that detections could follow both P-phase or S-phase rather than consistantly being sensitive to only one particular phase. It also showed that detections generally occurred between 0 - 5 seconds after the peak ground acceleration measured by the seismic station. Analysis was conducted on 550 detections made by the EQN system between 2017-12-15 and 2020-01-31 in Chile, Italy and the USA. Strong motion accelerometer data was collected from seismic stations via the FDSN protocol. The data was calibrated, detrended and a small time shift was applied to correct for differences in distances from the epicentre between the EQN detection and the strong motion seismic station. Calibrated waveform data was obtained for 410 EQN detections. Plots were made for each event and an analysis was carried out on the dataset to compare EQN detection times with the peak ground acceleration measured by the nearest seismic station. The dataset consists of a zip-file containing a table of results and some summary graphs derived from it as well as a set of 410 graphs of strong motion files that are presented as image files (png-files). The graphs show the waveform data for a seismic station within 20 km of each EQN detection.
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