122 documents found in 429ms
# 1
Rudenko, Sergei • Schöne, Tilo • Esselborn, Saskia • Neumayer, Karl Hans
Abstract: The data set provides GFZ VER13 orbits of altimetry satellites: ERS-1 (August 1, 1991 - July 5, 1996),ERS-2 (May 13, 1995 - February 27, 2006),Envisat (April 12, 2002 - April 8, 2012),TOPEX/Poseidon (September 23, 1992 - October 8, 2005),Jason-1 (January 13, 2002 - July 5, 2013) andJason-2 (July 5, 2008 - April 5, 2015) derived at the time spans given at the GFZ German Research Centre for Geosciences (Potsdam, Germany) within the Sea Level phase 2 project of the European Space Agency (ESA) Climate Change Initiative using "Earth Parameter and Orbit System - Orbit Computation (EPOS-OC)" software (Zhu et al., 2004) and the Altimeter Database and processing System (ADS, http://adsc.gfz-potsdam.de/ads/) developed at GFZ. The orbits were computed in the ITRF2014 terrestrial reference frame for all satellites using common, most precise models and standards available and described below. The ERS-1 orbit is computed using satellite laser ranging (SLR) and altimeter crossover data, while the ERS-2 orbit is derived using additionally Precise Range And Range-rate Equipment (PRARE) measurements. The Envisat, TOPEX/Poseidon, Jason-1, and Jason-2 orbits are based on Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) and SLR observations. For Envisat, altimeter crossover data were used additionally at 44 of 764 orbital arcs with gaps in SLR and DORIS data. The orbit files are available in the Extended Standard Product 3 Orbit Format (SP3-c). Files are gzip-compressed. File names are given as sate_YYYYMMDD_SP3C.gz, where "sate" is the abbreviation (ENVI, ERS1, ERS2, JAS1, JAS2, TOPX) of the satellite name, YYYY stands for 4-digit year, MM for month and DD for day of the beginning of the file. More details on these orbits are provided in Rudenko et al. (2018) to which these orbits are supplementary material.
# 2
Vey, Sibylle • Güntner, Andreas • Wickert, Jens • Blume, Theresa • Thoss, Heiko • (et. al.)
Abstract: We provide data of a case study from the GNSS station Wettzell, Germany (WTZR). This data set contains snow depth derived from GNSS data using reflectometry. It covers a time period from July 1, 2012 to July 1, 2015 and gives the integral snow depth over an area of about 150 by 30 m. The data are daily averages based on daily measurements from 4 different satellites. The GNSS derived snow depth was validated by observations from ultrasonic sensors (US). The detailed description of the processing, the evaluation with US and the discussion of the results is described in Vey et al. (2016). The data are provided in ASCII format with four colums: GNSS data (file Vey-et-al-2016-GNSS_2012_15.txt): (1) year (YEAR) (2) day of the year (DOY) (3) snow depth (SD cm) from GNSS (4) accuracy, root mean square error (RMSE cm) Ultrasonic Sensor data (file Vey-et-al-2016-US_2012_15..txt): (1) year (YEAR) (2) day of the year (DOY) (3) SD_US_pillow (cm) snow depth from the US sensor located above snow pillow (4) SD_US_SPA(cm) snow depth from the US sensor located at the snow pack analyzer
# 3
Frick, Daniel A. • Schuessler, Jan A. • Sommer, Michael • von Blanckenburg, Friedhelm
Abstract: Silicon is a beneficial element for many plants, and is deposited in plant tissue as amorphous bio-opal (phytoliths). The biochemical processes of uptake and precipitation induce isotope fractionation: the mass-dependent shift in the relative abundances of the stable isotopes of silicon. At the bulk scale, the silicon isotope composition reported as δ30Si span from -2 to +6 ‰. To further constrain these variations, at the scale of individual phytolith fragments we applied in situ femtosecond laser ablation multicollector inductively coupled plasma mass spectrometry (fsLA-MC-ICP-MS) to a set of 7 natural phytolith samples. Two phytoliths samples (Norway spruce Picea abies and European beech Fagus sylvatica L.) were extracted from the organic-rich topsoil horizon (O) of two studies sites in Germany (Beerenbusch, close to village Rheinsberg and Wildmooswald, in the southern Black Forest). The other five phytolith samples (bushgrass Calamagrostis epigejos, common reed Phragmites australis, common horsetail Equisetum arvense, annual and perennial rough horsetail Equisetum hyemale) were separated from plant materials. The individual phytolith fragments were analysed by fsLA-MC-ICP-MS and Si isotope results are reported in the δ-notation (delta) as permil deviation relative to NIST SRM610, which is isotopically indistinguishable from the reference material NBS28 (quartz NIST SRM8546 alias NBS28, δ29Si ≡ 0 and δ30Si ≡ 0). Raw data processing and background corrections were made according to the protocol described in Schuessler and von Blanckenburg (2014) that also involves application of several data rejection/acceptance criteria. Of these, the most important ones are that A) only 30/28Si and 29/28Si ratios are used for the calculation which deviate less than 3 standard deviation from the mean and B) only results which follow the mass-depended terrestrial fractionation line in a three-isotope-plot of δ29Si vs. δ30Si within analytical uncertainties and C) have a mass bias drift between the two bracketing standards of less than 0.30 ‰ in 30/28Si are accepted and reported in this study. Detailed description of the sample origin, preparation steps, and the measurement protocol can be found in Frick, D. A.; Schuessler, J. A.; Sommer, M.; von Blanckenburg, F. (2018): Laser ablation in situ silicon stable isotope analysis of phytoliths. Geostandards and Geoanalytical Research. https://doi.org/10.1111/ggr.12243. With this supplement we aim to provide a comprehensive dataset for in situ stable silicon isotope composition of individual phytolith fragments.
# 4
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.
# 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
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).
# 7
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).
# 8
Yan, Rui • Woith, Heiko • Wang, Rongjiang • Wang, Guangcai
Abstract: A high-fidelity radon record covering nearly 40 years from the hot spring site of BangLazhang (BLZ), Southwestern China allows to study multi-year periodicities. At BLZ, radon dissolved in water (Radon), water temperature (WT), and spring discharge rate (DR) were measured daily from 1976 until 2015. Barometric pressure, regional rainfall, galactic cosmic rays (GCR flux is modulated by solar wind and thus a proxy for solar activity), and regional seismicity from the same period were considered to identify potentially influencing factors controlling the changes in radon [Yan et al., 2017]. Various wavelet techniques indicate that the long-period radon concentration is characterized by a quasi-decadal (8-11 years) cycle, matching well with the concurrent periodicity in water temperature, spring discharge rates. The BLZ hot spring monitoring site is maintained and operated by the China Earthquake Administration of Yunnan Province. Water from the spring is sampled once daily and measurements of radon have been performed routinely in a laboratory since 1976 April 6. The sample time is designated to occur at 8 o’clock in the morning in order to reduce the effect of daily variations. The radon concentration has been measured with three types of radon measurement instruments during the past 40 years. From 1976 April 6, to 1982 June 5, a FD-105 type radon gas detector was used, reporting the radon concentration in Eman. Eman is converted to the metric unit Bq/L using the relationship 1 Eman = 3.7 Bq/L. From 1982 June 6 to 2012 April 11, a FD-105K type electrometer (manufactured by Shanghai Electronic Instrument, co.) was used, the measurements given in Bq/L. Since 2012 April 12, a FD-125 type Radon & Thorium analyzer, manufactured by Beijing Nuclear Instrument Factory, sponsored by CNNC (China National Nuclear Corporation), has been used. Water sampled from the spring is degassed by bubbling air and transported into a chamber, where the radon concentration is measured in a ZnS cell connected to a photomultiplier detector, and a scintillation counter. The measurement precision of the instruments is 0.1 Bq/L. A solid radium source (226Ra) with a known radioactive radon content is used for the calibration of the water radon under normal working conditions. This source is used to measure and calculate the calibration value of the instrument. In addition to radon, water temperature and spring discharge rate are measured at the spring site when the water is sampled for radon. Temperature is measured using a mercury thermometer with a resolution of 0.1°C. Discharge rate is measured using the stopwatch capacity method, i.e., the required time per unit volume of water is measured. Barometric pressure has been measured since 1997. Regional rainfall data were downloaded through the CPC Merged Analysis of Precipitation (CMAP) for the same period to evaluate its possible influence on radon in the present study.
# 9
Rosenau, Matthias • Horenko, Illia • Corbi, Fabio • Rudolf, Michael • Kornhuber, Ralf • (et. al.)
Abstract: This data set provides data from subduction zone earthquake experiments and analysis described in Rosenau et al. (2019). In the experiments analogue seismotectonic scale models of subduction zones characterized by two seismogenic asperities are used to study the interaction of asperities over multiple seismic cycles by means of static (Coulomb failure) stress transfer. Various asperity geometries (lateral/along-strike of the subduction zone distance and vertical/across-strike of the subduction zone offset) are tested on their effect on recurrence pattern of simulated great (M8+) earthquakes. The results demonstrate the role of stress coupling in the synchronization of asperities leading to multi-asperity M9+ events in nature. The data set contains time series of experimental surface velocities from which analogue earthquakes are detected and classified into synchronized events and solo events. The latter are subcategorized into main events and aftershocks and into normal and thrust events. An analogue earthquake catalogue lists all categorized events of the 12 experiments used for statistical analysis. Moreover, results from elastic dislocation modelling aimed ate quantifying the stress coupling between the asperities for the various geometries are summarized. Basic statistics of classified events (e.g. percentage of categorized events, coefficient of variation in size and recurrence time etc.) are documented. Matlab scripts are provided to visualize the data as in the paper.
# 10
Blanke, Aglaja • Kwiatek, Grzegorz • Martínez-Garzón, Patricia • Bohnhoff, Marco
Abstract: This data set is supplementary to the BSSA research article of Blanke et al. (2019), in which the local S-wave coda quality factor at The Geysers geothermal field, California, is investigated. Over 700 induced microseismic events recorded between June 2009 and March 2015 at 31 short-period stations of the Berkeley-Geysers Seismic Network were used to estimate the frequency-dependent coda quality factor (Q_C) using the method of Phillips (1985). A sensitivity analysis was performed to different input parameters (magnitude range, lapse time, moving window width, total coda length and seismic sensor component) to gain a better overview on how these parameters influence Q_C estimates. Tested parameters mainly show a low impact on the outcome whereas applied quality criteria like signal-to-noise ratio and allowed uncertainties of Q_C estimates were found to be the most sensitive factors. Frequency-dependent mean-Q_C curves were calculated from seismograms of induced earthquakes for each station located at The Geysers using the tested favored input parameters. The final results were tested in the context of spatio-temporal behavior of Q_C in the reservoir considering distance-, azimuth and geothermal production rate variations. A distance and azimuthal dependence was found which is related to the reservoir anisotropy, lithological-, and structural features. By contrast, variations in geothermal production rates do not influence the estimates. In addition, the final results were compared with previous estimated frequency-independent intrinsic direct S-wave quality factors (Q_D) of Kwiatek et al. (2015). A match of Q_D was observed with Q_C estimates obtained at 7 Hz center-frequency, suggesting that Q_D might not be of an intrinsic but of scattering origin at The Geysers. Additionally, Q_C estimates feature lower spreading of values and thus a higher stability. The Geysers geothermal field is located approximately 110 km northwest of San Francisco, California in the Mayacamas Mountains. It is the largest steam-dominated geothermal reservoir operating since the 1960s. The local seismicity is clearly related to the water injections and steam production with magnitudes up to ~5 occurring down to 5 km depth, reaching the high temperature zone (up to 360°C). The whole study area is underlain by a felsite (granitic intrusion) that shows an elevation towards the southeast and subsides towards northwest. A fracture network induces anisotropy into the otherwise isotropic rocks featuring different orientations. Moreover, shear-wave splitting and high attenuating seismic signals are observed and motivate to analyze the frequency-dependent coda quality factor. Two data sets were analyzed: one distinct cluster located in the northwest (NW) close to injection wells Prati-9 and Prati-29, and the other one southeast (SE) of The Geysers, California, USA, close to station TCH (38° 50′ 08.2″ N, 122° 49′ 33.7″ W and 38° 46′ 59.5″ N, 122° 44′ 13.2″ W, respectively). The frequency-dependent coda quality factor is estimated from the seismic S-wave coda by applying the moving window method and regression analysis of Phillips (1985). Different input parameters including moving widow width, lapse time and total coda length are used to obtain Q_C estimates and associated uncertainties. Within a sensitivity analysis we investigated the influence of these parameters and also of magnitude ranges and seismic sensor components on Q_C estimates. The coda analysis was performed for each event at each sensor component of each station. The seismograms were filtered in predefined octave-width frequency bands with center-frequencies ranging from 1-69 Hz. The moving window method was applied starting in the early coda (after the S-onset) for each frequency band measuring the decay of Power Spectral Density spectra. The decay of coda amplitudes was fitted with a regression line and Q_C estimates were calculated from its decay slope for each frequency band. In a final step a mean-Q_C curve was calculated for each available station within the study area resulting in different curves dependent on event location sites in the northwest and southeast. Data Description The data contain final mean-Q_C estimates of the NW and SE Geysers, coda Q estimates at 7 Hz center-frequency calculated by using the NW cluster, and initial direct Q estimates of Kwiatek et al. (2015) using the same data of the NW cluster. Table S1 shows final mean coda quality factor estimates obtained from the NW cluster at injection wells Prati-9 and Prati-29. The column headers show stations (station), center-frequencies of octave-width frequency bands in Hertz (f[Hz]), mean coda Q estimates (meanQc) and related standard deviations (std), all obtained by coda analysis. Table S2 shows the final mean coda quality factor estimates obtained from additional selected 100 events in the SE Geysers. Column headers correspond to those in Table S1. Table S3 shows coda Q estimates related to 7 Hz center-frequency. The column headers show stations (station), center-frequency of octave-width frequency bands in Hertz (f[Hz]), coda Q estimates at 7 Hz center-frequency (Q_C) and related standard deviations (std2sigma; 95% confidence level), all obtained by coda analysis. Table S4 shows selected direct S-wave quality factors of Kwiatek et al. (2015) obtained by spectral fitting. The column headers show stations (station) and direct S-wave Q estimates (Q_D). The four tables are provided in tab separated txt format. Tables S3 and S4 are used for a comparative study and displayed in Figure 12 of the BSSA article mentioned above.
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