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This data collection contains six inundation maps in Lima and Callao (Peru) based on tsunami simulations with the wave propagation and run-up model TsunAWI (see Rakowsky et al. 2015). The simulations were carried out in the framework of the RIESGOS project (see riesgos.de). The sources are hypothetical earthquake events in the magnitude range Mw 8.5 to Mw 9.0 offshore Lima. The source area of the events is based on the historical event from October 1746, the parameters are derived from the study Jimenez et al. (2013). The sources are considerably simplified since we aim at a systematic investigation of the tsunami impact and restrict the parameter variation between scenarios to one parameter only, the slip value. The source area is split into five subfaults, however we use a constant slip distribution. The corresponding tsunami simulations are carried out in a triangular mesh with resolution ranging from 7km in the deep ocean to a finest value of about 7m in the coastal land part of the pilot area Lima/Callao. The flow depth distribution in Lima/Callao obtained from the simulation is interpolated to a raster file and provided as Golden Software Binary Grids. The numerical results are obtained from simulations with the finite element model TsunAWI (Rakowsky et al. 2015). The mesh resolution in the pilot area Lima/Callao is approximately 20m, the smallest edge length is about 7m. The main model parameters are listed in Table 1. Concerning the bottom roughness, we use a constant Manning coefficient of 0.02 in all of the model domain.
Multi-resolution exposure model for seismic risk assessment in Turkmenistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Turkmenistan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process) For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
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.
Despite the exponential growth of the amount of ground‐motion data, ground‐motion records are not always available for all distances, magnitudes, and site conditions cases. TFCGAN is a Python software package for modeling and simulating ground shaking to tackle this problem. Based on Esfahani et al. 2023, the software can be used as library in custom code or as command line application and can generate ground-shaking records in different domains (Fourier, Time-Frequency, and Time domains) and different formats (currently numpy, ascii, with foreseen implementation of other formats such as ASDF). The enclosed code and model consist of two steps. In the first step, the generative model simulates ground shaking by conditioning on a set of parameters. In the second step, the time-frequency domain is transferred to the time domain based on the phase retrieval algorithm. The model is conditioned on moment magnitude, distance, and shear wave velocity at the near-surface and trained using the KiK-net database. The proposed model is extended by using a hybrid dataset based on the combination of the European strong motion (ESM) database, near-fault ground-shaking records, and synthetic records. We validate our model based on terms of standard deviations for peak ground accelerations and Fourier amplitude spectral values.
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.
Multi-resolution exposure model for seismic risk assessment in the Kyrgyz Republic. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of 1'175 geo-cells covering the territory of the Kyrgyz Republic. The model integrates around 6'000 building observations (see related dataset Pittore et al. 2019). The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
The Early-Warning and Rapid Impact Assessment with real-time GNSS in the Mediterranean (EWRICA) is a federal Ministry of Education and Research funded project (funding period: 2020-2023) that aims to develop fast kinematic and point source inversion and modeling tools combining GNSS-based near field data with traditional broadband ground velocity and accelerometer data. Fast and robust estimates of seismic source parameters are essential for reliable hazard estimates, e.g. in the frame of tsunami early warning. Hence, EWRICA aims for the development and testing of new real time seismic source inversion techniques based on local surface displacements. The resulting methods shall be applied for tsunami early warning purposes in the Mediterranean area. In this framework, this repository is a suite of four packages that can be used and combined in different ways and are ewricacore, ewricasiria, ewricagm and ewricawebapp. These four packages can be deployed in a docker container (see instructions below) to demonstrate a possible output of Early-Warning and Rapid Impact Assessment. In the Docker, a probabilistic earthquake source inversion report (ewricasiria) and a Neural network based Shake map (ewricagm) are generated for two past earthquakes whose data (event and waveform) is continuously served by GEOFON servers at regualr intervals to produce and test a real case scenario. The whole workflow is managed by ewricacore, a central unit of work that first fetches the waveform data via the seedlink protocol and event data via event bus or FDSN web service, then collects and cuts waveforms segments according to a custom configuration, and eventually triggers custom processing (ewricasiria and ewricagm in the docker, but any processing can be implemented) whenever configurable conditions are met. The final package, ewricawebapp is a web-based graphical user interface that can be opened in your local browser or deployed on your web server in order to visualize and check all output produced by the docker workflow in form of HTML pges, images and data in various formats (e.g., JSON, log text files). The EWRICA Docker package includes the following tools: ewricacore: Central unit for all Ewrica components and event/data listener ewricagm: Create ground motion maps via pre-trained Neural Network ewricasiria: Ewrica Source Inversion and Rapid Impact Assessment Python package ewricawebapp: Ewrica web portal and GUI demo grond: A probabilistic earthquake source inversion framework (Heimann et al., 2018) stationsxml-archive: Storage repository for synchronizing Station XMLs
Multi-resolution exposure model for seismic risk assessment in Kazakhstan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Kazakhstan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Kyrgyzstan and Tajikistan, within the framework of the projects EMCA (Earthquake Model Central Asia), funded by GEM, and "Assessing Seismic Risk in the Kyrgyz Republic", funded by the World Bank. The survey has been carried out between 2012 and 2016 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images and footprints from OpenStreetMap. The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). The following attributes are defined (not all are observable in the RRVS survey): code, description: lon, longitude in fraction of degrees lat, latitude in fraction of degrees object_id, unique id of the building surveyed MAT_TYPE,Material Type MAT_TECH,Material Technology MAT_PROP,Material Property LLRS,Type of Lateral Load-Resisting System LLRS_DUCT,System Ductility HEIGHT,Height YR_BUILT,Date of Construction or Retrofit OCCUPY,Building Occupancy Class - General OCCUPY_DT,Building Occupancy Class - Detail POSITION,Building Position within a Block PLAN_SHAPE,Shape of the Building Plan STR_IRREG,Regular or Irregular STR_IRREG_DT,Plan Irregularity or Vertical Irregularity STR_IRREG_TYPE,Type of Irregularity NONSTRCEXW,Exterior walls ROOF_SHAPE,Roof Shape ROOFCOVMAT,Roof Covering ROOFSYSMAT,Roof System Material ROOFSYSTYP,Roof System Type ROOF_CONN,Roof Connections FLOOR_MAT,Floor Material FLOOR_TYPE,Floor System Type FLOOR_CONN,Floor Connections For each building an EMCA vulnerability class has been assigned following the fuzzy scoring methodology described in Pittore et al., 2018. The related class definition schema (as a .json document) is included in the data package.
Multi-resolution exposure model for seismic risk assessment in Uzbekistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Uzbekistan (provided as a separate file). The model prior is based on empirical observations in Kyrgyzstan and Tajikistan as well as user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
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