11 documents found in 313ms
# 1
GEOFON Data Centre
Abstract: P-phase arrival times automatically created by the SeisComP3 (https://www.seiscomp3.org/) software at the GFZ scanning all stations available in real-time at GEOFON Data Centre and listed in the contributors list. Data have been used in the publication by Steed et al 2018 to test the new CsLoc method, sent in relatime to EMSC using the HMB application (Heinloo, 2016, http://doi.org/10.5880/GFZ.2.4.2016.001). The data sets includes P-phases from 2016 and 2017. The data are presented in two csv tables (part I and part II) that are included in the folder 2018-002_Geofon_csloc_test_phases.zip.
# 2
Pittore, Massimiliano • Haas, Michael • Megalooikonomou, Konstantinos
Abstract: The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS (Remote Rapid Visual Screening) methodology in Alsace, France, within the framework of the DESTRESS project. The survey has been carried out between May and June 2017 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: February 2011) and footprints from OpenStreetMap.Surveyor: Konstantinos G. Megalooikonomou (GFZ German Research Centre for Geosciences)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,descriptionlon, longitude in fraction of degreeslat, latitude in fraction of degreesobject_id, unique id of the building surveyedMAT_TYPE,Material TypeMAT_TECH,Material TechnologyMAT_PROP,Material PropertyLLRS,Type of Lateral Load-Resisting SystemLLRS_DUCT,System DuctilityHEIGHT,HeightYR_BUILT,Date of Construction or RetrofitOCCUPY,Building Occupancy Class - GeneralOCCUPY_DT,Building Occupancy Class - DetailPOSITION,Building Position within a BlockPLAN_SHAPE,Shape of the Building PlanSTR_IRREG,Regular or IrregularSTR_IRREG_DT,Plan Irregularity or Vertical IrregularitySTR_IRREG_TYPE,Type of IrregularityNONSTRCEXW,Exterior wallsROOF_SHAPE,Roof ShapeROOFCOVMAT,Roof CoveringROOFSYSMAT,Roof System MaterialROOFSYSTYP,Roof System TypeROOF_CONN,Roof ConnectionsFLOOR_MAT,Floor MaterialFLOOR_TYPE,Floor System TypeFLOOR_CONN,Floor Connections
# 3
Heimann, Sebastian • Dahm, Torsten • Hensch, Martin • Ritter, Joachim • Schmidt, Bernd • (et. al.)
Abstract: 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.
# 4
Jousset, Philippe • Reinsch, Thomas • Ryberg, Trond • Blanck, Hanna • Clarke, Andy • (et. al.)
Abstract: Imaging the internal structure of faults remains challenging using conventional seismome-ters. Here, the authors use deployed fibre-optic cables to obtain strain data and identify faults and volcanic dykes in Iceland. Such fibre-optic networks are pervasive for telecommu-nication and could be used for hazard assessment. Natural hazard prediction and efficient crustal exploration requires dense seismic observa-tions both in time and space. Seismological techniques provide ground-motion data, whose accuracy depends on sensor characteristics and spatial distribution. In the manuscript Jousset et al. (2018), we demonstrate that strain determination is possible with conventional fibre-optic cables deployed for telecommunication. Extending recently distributed acoustic sensing (DAS) studies, we present high resolution spatially un-aliased broadband strain data. We recorded seismic signals from natural and man-made sources with 4-m spacing along a 15-km-long fibre-optic cable layout on Reykjanes Peninsula, SW Iceland. This data publication contains data used for plotting several figures of Jousset et al. (2018). For further explanation of the data and related processing steps, please refer to Jousset et al. (2018). A theoretical study with respect to the coupling of the cable to the ground has been published by Reinsch et al. (2017).
# 5
Grünthal, Gottfried • Stromeyer, Dietrich • Bosse, Christian
Abstract: The main input data of the earthquake model for the probabilistic seismic hazard assessment of Germany, version 2016, are provided in form of: (1) the geometry of five areal source zone models and one composite fault model for the Lower Rhine graben (ESRI shape files) and (2) the seismicity rates of all sources given as Mmax-depending Gutenberg-Richter parameters a and b with their uncertainties (EXCEL and csv files).The assignment of individual sources to the superzone models of Mmax, b-value, depth, tectonic regime and smoothing kernel arer described in the accompanying Scientific Technical Report Data (Grünthal et al. 2017).
# 6
Reinsch, Thomas • Blöcher Guido • Kranz, Stefan
Abstract: This data is documented by the Scientific Technical Report Data 15/02 (http://dx.doi.org/10.2312/GFZ.b103-15021). Both, the data and the report, are supplements to the publication Blöcher et al. (2015), accessible via http://dx.doi.org/10.1016/j.geothermics.2015.07.008. From 2011-06-01 until 2013-12-31, the measurement and control system at the Groß Schönebeck research platform acquired data from several circulation experiments. Different data values were recorded at a sampling interval of 1 s. Relevant data for understanding and analyzing the hydraulic situation of the system were resampled to a 1 minute interval. From the resampled dataset, additional parameters were derived. Furthermore, if parameters were considered to be essential, but the measurement of these parameters was erroneous, some data were reprocessed. All relevant data and processing steps performed on the data are described within this report. Data described within this report can be accessed via http://dx.doi.org/10.5880/GFZ.b103-15021.1. The presented data was acquired during different research projects by the staff of the International Centre for Geothermal Research as well as Section 4.1 Reservoirtechnologies at the Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences.
# 7
Moreno, Marcos • Bedford, Jonathan • Baez, Juan Carlos • Klotz, Jürgen • Hoffmann, Felix • (et. al.)
Abstract: The survey-mode GPS (sGPS) network in the IPOC region consists of 91 geodetic markers. Over the last decade, the positions of these points in the network have been periodically measured, thus enabling us to quantify the decadal patterns of deformation processes. This temporal catalogue of coordinates complement the continuous GPS (cGPS) array. Meta-data and raw data in Rinex format for the surveys carried out in 2008, 2011, 2013, 2014, 2015, and 2016 are available for 91 sites in the north of Chile and the northwest of Argentina. Included in this temporal catalogue are observations made shortly after the 2014 Pisagua-Iquique earthquake. Detailed information about data availability, metadata and site descriptions can be found at: https://kg189/gnss/IPOCSGPS. More description about the Integrated Plate Boundary Observatory Chile (IPOC) can be found at the IPOC Website (www.ipoc-network.org) and on the sGPS Survey on www.ipoc-network.org/associated-projects/gps-campaigns/.
# 8
Heimann, Sebastian • Kriegerowski, Marius • Isken, Marius • Cesca, Simone • Daout, Simon • (et. al.)
Abstract: Pyrocko is an open source seismology toolbox and library, written in the Python programming language. It can be utilized flexibly for a variety of geophysical tasks, like seismological data processing and analysis, calculation of Green's functions and earthquake models' synthetic waveforms and static displacements (InSAR or GPS). Those can be used to characterize extended earthquake ruptures, point sources (moment tensors) and other seismic sources. This publication includes the Pyrocko core, a library providing building blocks for researchers and students wishing to develop their own applications. The Pyrocko framework also ships with application: (1) Snuffler (interactive seismogram browser and workbench), (2) Cake (1D travel-time and ray-path computations), (3) Fomosto (calculate and manage Green’s function databases) and (4) Jackseis (waveform archive data manipulation). Additional applications, as of Grond, Lassie and Kite are individual software publications. See the project page (www.pyrocko.org) for full documentation, tutorials and installation instructions.
# 9
Richter, Nicole • Favalli, Massimiliano • de Zeeuw-van Dalfsen, Elske • Fornaciai, Alessandro • da Silva Fernandes, Rui Manuel • (et. al.)
Abstract: We provide an updated lava flow hazard map for Fogo Volcano, Cabo Verde that is valid after the 2014-2015 eruptive crises. The hazard map shows the probability of lava flow invasion within the Chã das Caldeiras and on the eastern flank of the volcano. This probability is defined as the likelihood that a future lava flow will inundate a specific point before the vent location is known. The hazard map is calculated on the basis of a 5 m resolution digital elevation model generated from contours on the base of photogrammetric data that was updated for the 2014-2015 lava flow using combined terrestrial laser scanner (TLS) and camera data. The lava flow hazard map in printable A0 poster format is available in two versions, an English-Kreolu version (blue) and an English-Portugese version (green). Please refer to Richter et al. (2016) for more information and scientific background, as well as for supplementary material in kml format.
# 10
Motagh, Mahdi • Shamshiri, Roghayeh • Haghshenas-Haghighi, Mahmud • Wetzel, Hans-Ulrich • Akbari, Bahman • (et. al.)
Abstract: This data publication provides supplementary material to Motagh et. al (2017), which presents the results of an InSAR time series analysis obtained by the exploitation of Envisat, ALOS and Sentinel-1 (S1) SAR data archives between June 2004, and May 2016. The study investigates land subsidence due to groundwater overexploitation for agriculture and industrial development in the Rafsanjan plain of southeastern Iran. Datasets included are a list of SAR data used for the study and the Line of Sight (LOS) displacement rates from Envisat, ALOS and Sentinel 1 (S1) satellites in ASCII format. More in formation is avalable in Motagh-et-al-2017-Supplementary_Material_readme.pdf.
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