118 documents found in 348ms
# 1
Soares, Gabriel B. • Matzka, Jürgen • Pinheiro, Katia
Abstract: This dataset comprises preliminary minute means of the XYZ (X=geographic north, Y=geographic east, Z=downward) magnetic field components measured at the geomagnetic observatory Tatuoca (IAGA code TTB) for the period June 1st, 2008 to December 31st, 2017. TTB is located in northern Brazil and operates under the administration of Observatório Nacional (ON) since 1957. Since 2015 it is operated in cooperation between ON and GFZ. Since the early years of the 2000 decade, the magnetic equator is close to the observatory TTB. The variations from June 1st 2008 until November 19th, 2015 were recorded by a LEMI-417M fluxgate magnetometer (sampling rate during most of this period was 1 sec, but occasionally 0.25 or 6 sec). From November 20th, 2015 onwards, a DTU FGE fluxgate magnetometer (1 sec sampling) provided the variations. From late October 2016, the total field F was measured with a Gemsys overhauser absolute scalar magnetometer with 1 sec sampling. This is a processed and calibrated dataset. Inconsistencies like spikes and data jumps were corrected. The maximum admitted noise level in this dataset is 1 nT peak to peak in the underlying 1 sec data. Periods of recurrent noise exceeding this criterion were systematically deleted from the records. For data calibration, the baseline was constructed by means of absolute measurements of the geomagnetic field and applied to the variation data. The data files are provided in the IAGA-2002 format (https://www.ngdc.noaa.gov/IAGA/vdat/IAGA2002/iaga2002format.html) as daily files for 1-minute means. Following the IAGA2002-format, the filename consists of the IAGA-code, the year (YYYY), the month (MM), the day (DD), the letter p for preliminary, the letters min for 1-minute data, and the file extension min again for 1-minute data. The first 16 lines in each file are a IAGA2002-typical header, then comes, blank separated, the date (YYYY-MM-DD), time (hh:mm:ss.sss in UTC), day of year (DOY), the X component (XXXXX.XX in nT), the Y-component (YYYYY.YY in nT), the Z-component (ZZZZZ.ZZ in nT) and F (FFFFF.FF in nT). Please note that a dataset based on the data provided here will be submitted to the World Data Centre for Geomagnetism (WDC Edinburgh) at a later stage and might undergo further modifications. Geomagnetic observatories in general are described in e.g. Jankowski and Sucksdorff (1996), Matzka et al. (2010). GFZ observatories and observatory cooperations are described in Matzka (2016). The Geomagnetic Observatory Tatuoca (TTB) is described in Moschhauser et al. (2017).
# 2
Unger, Andrea • Rabe, Daniela • Klemann, Volker • Eggert, Daniel • Dransch, Doris
Abstract: The validation of a simulation model is a crucial task in model development. It involves the comparison of simulation data to observation data and the identification of suitable model parameters. SLIVISU is a Visual Analytics framework that enables geoscientists to perform these tasks for observation data that is sparse and uncertain. Primarily, SLIVISU was designed to evaluate sea level indicators, which are geological or archaeological samples supporting the reconstruction of former sea level over the last ten thousands of years and are compiled in a postgreSQL database system. At the same time, the software aims at supporting the validation of numerical sea-level reconstructions against this data by means of visual analytics.
# 3
Ritter, Patricia
Abstract: This dataset comprises profiles of Hall ionospheric current densities derived from scalar magnetic field data measured from the CHAMP satellite during six magnetic storms. The Hall currents are intense electric currents that flow horizontally above the earth’s surface in the polar region and perpendicular to the geomagnetic field. They peak at approximately ± 80° of geomagnetic latitude. Together with the field-aligned currents they form part of the ionospheric current system. During enhanced geomagnetic activity the Hall current peak locations are shifted equatorward. The CHAllenging Minisatellite Payload (CHAMP) spacecraft circled the Earth during the years 2000 – 2010 on a near-polar orbit (inclination 87.3°), each orbit taking 93 minutes at an altitude of initially 455 km. Within 4 months CHAMP covered all local times. The data records used for determining the Hall currents are scalar magnetic field measurements obtained with the Overhauser magnetometer on the satellite boom, with a sample frequency of 1 Hz and a resolution of 0.1 nT. In order to isolate the magnetic effects of ionospheric currents in the satellite observations, the contributions from all other sources were removed from the scalar field readings. The main, crustal and external magnetic fields were subtracted using the POMME 6 model (Maus et al, 2010, http://www.geomag.us/models/pomme6.html). The Hall current densities were obtained by fitting a line current model to the observed magnetic field residuals. The model consists of a series of 160 horizontal infinite current lines centered at the orbit position closest to the geographic pole, at an altitude of 110 km and separated by 1° in latitude. The magnetic field of the line currents are related to the current strength according to the Biot–Savart law. Assuming a static current, the strength of each current line is derived from an inversion of the observed field residuals applying a least squares fitting approach. This method of Hall current estimation from scalar magnetometer records measured at satellites was proposed initially by Olsen (1996). The reliability of the approach was demonstrated and validated in a statistical study where Hall current density estimates from CHAMP were directly compared with independent determinations from ground observations of the IMAGE magnetometer array (Ritter et al., 2004).
# 4
Scherler, Dirk • Wulf, Hendrik • Gorelick, Noel
Abstract: This dataset is supplementary to the article of Scherler et al. (submitted), in which the global distribution of supraglacial debris cover is mapped and analyzed. For mapping supraglacial debris cover, we combined glacier outlines from the Randolph Glacier Inventory (RGI) version 6.0 (RGI consortium, 2017) with remote sensing-based ice and snow identification. Areas that belong to glaciers but that are neither ice nor snow were classified as debris cover. This dataset contains the outlines of the mapped debris-covered glaciers areas, stored in shapefiles (.shp). For creating this dataset, we used optical satellite data from Landsat 8 (for the time period 2013-2017), and from Sentinel-2A/B (2015-2017). For the ice and snow identification, we used three different algorithms: a red to short-wavelength infrared (swir) band ratio (RATIO; Hall et al., 1988), the normalized difference snow index (NDSI; Dozier, 1989), and linear spectral unmixing-derived fractional debris cover (FDC; e.g., Keshava and Mustard, 2002). For a detailed description of the debris-cover mapping and an analysis of the data, please see Scherler et al. (submitted). This dataset includes debris cover outlines based on either Landsat 8 (LS8; 30-m resolution) or Sentinel 2 (S2; 10-m resolution), and the three algorithms RATIO, NDSI, FDC. In total, there exist six different zip-files that each contain 19 shapefiles. The structure of the shapefiles follows that of the RGI version 6.0 (RGI consortium, 2017), with one shapefile for each RGI region. The original RGI shapefiles provide each glacier as one entry (feature) and include a variety of ancillary information, such as area, slope, aspect (RGI Consortium 2017a, Technical Note p. 12ff). Because the debris-cover outlines are based on the RGI v6.0 glacier outlines, all fields of the original shapefiles, which refer to the glacier, are retained, and expanded with four new fields: - DC_Area: Debris-covered area in m². Note that this unit for area is different from the unit used for reporting the glacier area (km²).- DC_BgnDate: Start of the time period from which satellite imagery was used to map debris cover.- DC_EndDate: End of the time period from which satellite imagery was used to map debris cover.- DC_CTSmean: Mean number of observations (CTS = COUNTS) per pixel and glacier. This number is derived from the number of available satellite images for the respective time period, reduced by filtering pixels due to cloud and snow cover. The dataset has a global extent and covers all of the glaciers in the RGI v. 6.0, but it exhibits poor coverage in the RGI region Subantarctic and Antarctic, where the debris cover extents are based on very few observations.
# 5
Fuchs, Sven • Förster, Hans-Jürgen • Braune, Kathleen • Förster, Andrea
Abstract: This data set compiles the raw data that were used to calculate the bulk thermal conductivity (λb) of low-porosity igneous rocks from modal mineralogy, porosity, and nature of saturation fluid. It compliments a paper by Fuchs et al. (2018) to which it represents supplementary material. The paper reports the result of seeking out the mixing model(s) providing the best match between measured (λb.meas) and calculated bulk thermal conductivity (λb.calc) for low-porous igneous rocks. The study encompassed 45 samples representing various geological provinces in eight countries. Our suite of samples covers the entire range from ultramafic (gabbro/diorite) to silicic rocks (granite), straddling the range 36–76 wt.% SiO2 (corresponding quartz range: 0–45 vol.%), and includes both such of alkaline, peralkaline, metaluminous, and peraluminous affinity. Assessment of the quality of fit involved all frequently applied mixing models that consider quantitative data on modal mineralogy. Our evaluation clearly demonstrates that λb of low-porous igneous rocks, irrespective of being ultramafic or felsic, could be indirectly calculated from their mineral content with an acceptable error by employing the harmonic mean model. We show that the use of the harmonic-mean (HM) model for both rock matrix and porosity provided a good match between λb.meas and λb.calc of < 10% deviation (2σ), with relative and absolute errors amounting to 1.4 ± 9.7% and 4.4 ± 4.9% respectively. The results of our study constitute a big step forward to a robust conclusion on the overall applicability of the HM model for inferring λb of low-porous, mafic to silicic magmatic and metamorphic rocks with an acceptable magnitude of error. The data included in this data publication are the tables and plots described in Fuchs et al. (2018). They are provided in Excel (.xlsx) and .csv Formats and are further described in the data description file. The diagrams are only included in the Excel version.
# 6
Unger, Andrea • Rabe, Daniela • Eggert, Daniel • Dransch, Doris
Abstract: Geoarchives are an important source to understand the interplay of climate and landscape developments in the past. One important example are sediment cores from the ground of lakes. The microfacies-explorer is a Java-based prototype, that provides a tailored combination of visual and data mining methods enabling scientists to explore categorical data from geoarchives.
# 7
Anderson, James M. • Xu, Ming H.
Abstract: Plots of closure delay, closure phase, and closure amplitude are provided for the geodetic very long baseline interferometry (VLBI) observations of the Continuous VLBI Campaign 2014 (CONT14) experiment of the International VLBI Service for Geodesy and Astrometry (IVS, https://ivscc.gsfc.nasa.gov/ , see https://ivscc.gsfc.nasa.gov/program/cont14/ for a description of CONT14, and see https://ivscc.gsfc.nasa.gov/about/org/components/dc-list.html for a list of IVS data centers from which the CONT14 data can be downloaded) as calculated by Anderson and Xu for their article titled _Source Structure and Measurement Noise Are as Important as All Other Residual Sources in Geodetic VLBI Combined_, submitted to the Journal of Geophysical Research - Solid Earth in 2018. Closure quantities are insensitive to station-based calibration terms, such as station clock errors, atmospheric delay errors, phase offsets, station position errors, amplitude calibration errors, and so on, and as a result are sensitive only to source structure (the two-dimensional brightness distribution of source emission on the sky, which is typically time and frequency dependent), measurement noise, and closure errors such as bandpass mismatch and polarization leakage. We used closure quantities derived from the CONT14 data to investigate the amount of source structure present in the celestial sources observed in the CONT14 experiment. Details: Three data files are included:(1) closure_delay_Anderson_Xu_JGR_2018.tar.gz(2) closure_phase_Anderson_Xu_JGR_2018.tar.gz(3) closure_amplitude_Anderson_Xu_JGR_2018.tar.gz The file with the name starting with "closure_delay" contains closure delay plots, the file with the name starting with "closure_phase" contains closure phase plots, and so on. These three files are collections of files made by the UNIX tar program that have been compressed with the gzip program.
# 8
Heimann, Sebastian • Isken, Marius • Kühn, Daniela • Sudhaus, Henriette • Steinberg, Andreas • (et. al.)
Abstract: Grond is an open source software tool for robust characterization of earthquake sources. Moment tensors and finite fault rupture models can be estimated from a combination of seismic waveforms, waveform attributes and geodetic observations like InSAR and GNSS. It helps you to investigate diverse magmatic, tectonic, and other geophysical processes at all scales. It delivers meaningful model uncertainties through a Bayesian bootstrap-based probabilistic joint inversion scheme. The optimisation explores the full model space and maps model parameter trade-offs with a flexible design of objective functions. Rapid forward modelling is enabled by using pre-computed Green's function databases, handled through the Pyrocko software library. They serve synthetic near-field surface displacements and synthetic seismic waveforms for arbitrary earthquake source models and geometries.
# 9
Ritter, Malte C. • Rosenau, Matthias • Oncken, Onno
Abstract: This dataset is supplementary material to the article of Ritter et al. (2017). In this article, the similarity of fault propagation work in analogue sandbox experiments to natural fault networks is investigated through measurements in a strike-slip sandbox and in a ring-shear-tester. The transient shear strength of the samples is measured for different fault lengths and from this the work is determined. For a detailed description of the procedure and the set-up please see Ritter et al. (2017). The data available in this supplementary publication are:• For the strike-slip experiments three video sequences of the deformation together with the evolution of boundary force for fault lengths of 20 cm, 30 cm and 40 cm. The videos show the curl of the deformation field, determined by Digital Image Correlation of top-view video images. These files are in AVI-format and included in the zip folder 2017-005-Ritter-movies.zip.• A folder containing force vs. displacement measurements for each experiment (2017-005-Ritter-forces.zip). These are 25 ASCII-files that contain two columns of numerical data: the first column is the displacement in meter; the second column is the corresponding force in newton. The files are named according to the following pattern: <fault length in meter>_<experiment number>.asc• A Matlab script to load the force files and calculate the work. This file is called “plotwork.m” and calls the Matlab function “work.m”, which does the actual calculations. These files have been tested in Matlab version 2012b. The surface deformation data are available upon request.
# 10
Ritter, Malte Christian • Santimano, Tasca • Rosenau, Matthias • Leever, Karen • Oncken, Onno
Abstract: This dataset is supplementary to the article of Ritter et al. (2017). In this article, a new experimental device is presented that facilitates precise measurements of boundary forces and surface deformation at high temporal and spatial resolution. This supplementary dataset contains the measurement data from two experiments carried out in this new experimental device: one experiment of an accretionary critical wedge and one of Riedel-type strike-slip deformation. For a detailed description of the set-up and an analysis of the data, please see Ritter et al. (2017). The data available for either experiment are:• A video showing deformation in top view together with the evolution of boundary force. This file is in AVI-format.• A time-series of 2D vector fields describing the surface deformation. These vector fields were obtained from top-view video images of the respective experiment by means of digital image correlation (DIC). Each vector field is contained in a separate file; the files are consecutively numbered. The vector fields are stored in *.mat-files that can be opened using e.g. the software Matlab or the freely available GNU Octave. They take the form of Matlab structure arrays and are compatible to the PIVmat-toolbox by Moisy (2016) that is freely available. The most important fields of the structure are: x and y, that are vectors spanning a coordinate system, and vx and vy, which are arrays containing the actual vector components in x- and y-direction, respectively.• A file containing the measurements of the boundary force applied to drive deformation. This file is also a *.mat-file, containing a structure F with fields force, velocity and position. These fields are vectors describing the force applied by the indenter, the indenter velocity and the indenter position
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