178 documents found in 344ms
# 1
Jatnieks, Janis • Sips, Mike • De Lucia, Marco • Dransch, Doris
Abstract: Geochemical models are used to seek answers about composition and evolution of groundwater, spill remediation, viability of geothermal resources and other important geoscientific applications. To understand these processes, it is useful to evaluate geochemical model response to different input parameter combinations. Running the model with varying input parameters creates a large amount of output data. It is a challenge to screen this data from the model to identify the significant relationships between input parameters and output variables. For addressing this problem we developed a Visual Analytics approach in an ongoing collaboration between Geoinformatics and Hydrogeology sections of GFZ German Research Centre for Geosciences. We implement our approach as an interactive data exploration tool called the GCex. GCex is a Visual Analytics approach and prototype that supports interactive exploration of geochemical models. It encodes many-to-many input/output relationships by the simple yet effective approach called Stacked Parameter Relation (SPR). GCex assists in the setup of simulations, model runs, data collection and result exploration, greatly enhancing the user experience in tasks such uncertainty and sensitivity analysis, inverse modeling and risk assessment. While in principle model-agnostic, the prototype currently supports and is tied to the popular geochemical code PHREEQC. Modification to support other models would not be complicated. GCex prototype was originally written by Janis Jatnieks at GFZ-Potsdam. It relies on Rphree (R-PHREEQC geochemical simulation model interface) written by Marco De Lucia at GFZ-Potsdam. A compatible version of Rphee is bundled with this installation.
https://gitext.gfz-potsdam.de/sec15pub/GCex/tags/1.0
# 2
Jatnieks, Janis • De Lucia, Marco • Sips, Mike • Dransch, Doris
Abstract: Surrogate playground is an automated machine learning approach written for rapidly screening a large number of different models to serve as surrogates for a slow running simulator. This code was written for a reactive transport application where a fluid flow model (hydrodynamics) is coupled to a geochemistry simulator (reactions in time and space) to simulate scenarios such as underground storage of CO2 or hydrogen storage for excess energy from wind farms. The challenge for such applications is that the geochemistry simulator is typically slow compared to fluid dynamics and constitutes the main bottleneck for producing highly detailed simulations of such application scenarios. This approach attempts to find machine learning models that can replace the slow running simulator when trained on input-output data from the geochemistry simulator. The code may be of more general interest as this prototype can be used to screen many different machine learning models for any regression problem in general. To illustrate this it also includes a demonstration example using the Boston housing standard data-set.
# 3
Mikhailova, Natalya • Poleshko, N.N. • Aristova,, I.L. • Mukambayev, A.S. • Kulikova, G.O.
Abstract: Version History11 Sep 2019: Release of Version 1.1 with the following changes: (1) new licence: CC BY SA 4.0, modification of the title: removal of file name and version); (2) addition of ORIDs when available. The metadata of the first version 1.0 is available in the download folder.. Data and file names remain unchanged. The EMCA (Earthquake Model Central Asia) catalogue (Mikhailova et al., 2015) includes information for 33620 earthquakes that occurred in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan). The catalogue provides for each event the estimated magnitude in terms of MLH (surface wave magnitude) scale, widely used in former USSR countries.MLH magnitudes range from 1.5 to 8.3. Although the catalogue spans the period from 2000 BC to 2009 AD, most of the entries (i.e. 33378) describe earthquakes that occurred after 1900. The catalogue includes the standard parametric information required for seismic hazard studies (i.e., time, location and magnitude values). The catalogue has been composed by integrating different sources (using different magnitude scales) and harmonised in terms of MLH scale. The MLH magnitude is determined from the horizontal component of surface waves (Rautian and Khalturin, 1994) and is reported in most of the seismic bulletins issued by seismological observatories in Central Asia. For the instrumental period MLH magnitude was estimated, when not directly measured, either from body wave magnitude (Mb), the energy class (K) or Mpva (regional magnitude by body waves determined by P-wave recorded by short-period instruments) using empirical regression analyses. The following relationships were used to estimate MLH (see Mikhailova, internal EMCA report, 2014):(1) MLH=0.47 K-1.15(2) MLH=1.34 Mb-1.89(3) MLH=1.14 Mpva-1.45When multiple scales were available for the same earthquake, priority was given to the conversion from K class. For the historical period, the MLH values were obtained from macroseismic information (Kondorskaya and Ulomov, 1996).
The catalogue is distributed as a ascii file in CSV (Comma Separated Value) format and UTF-8 encoding. A separate .csvt file is provided for column type specification (useful for importing the .csv file in QGIS and other similar environments).For each event the estimated location is provided as longitude, latitude, with the following spatial reference system: +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defsWhen possible, precise indication of the events´ time in UTC format are provided.Distribution file: "EMCA_SeismoCat_v1.0.csv" Version: v1.0 Release date: 2015-07-30Header of CSV file:id: (int) serial ID of the eventyear: (int) Year of the event. Negative years refer to BCE (Before Common Era / Before Christ) eventsmonth: (int, 1-12) Month of the year for the eventday: (int, 1-31) Day of the month for the eventhour : (int, 0-23) Hour of the daymin: (int, 0-59) Minute of the hoursec: (int, 0-59) Second (and hundredth of second, if available) of the minutelat: (float) Latitude of the eventlon: (float) Longitude of the eventfdepth: (int) Focal depth of event in kmmlh: (float) Surface wave magnitude (see e.g. Rautian T. and V. Khalturin, 1994)
# 4
Ullah, Shahid • Abdrakhmatov, Kanat • Sadykova, Alla • Ibragimov, Roman • Ishuk, Anatoly • (et. al.)
Abstract: Version History11 Sep 2019: Release of Version 1.1 with the following changes: (1) new licence: CC BY SA 4.0, modification of the title: removal of file name and version); (2) addition of ORIDs when available; (3) actualisation of affiliations for some authors The metadata of the first version 1.0 is available in the download folder.. Data and file names remain unchanged. Area Source model for Central AsiaThe area sources for Central Asia within the EMCA model are defined by mainly considering the pattern of crustal seismicity down to 50 km depth. Although tectonic and geological information, such as the position and strike distribution of known faults, have also been taken into account when available. Large area sources (see, for example source_id 1, 2, 5, 45 and 52, source ids are identified by parameter “source_id” in the related shapefile) are defined where the seismicity is scarce and there are no tectonic or geological features that would justify a further subdivision. Smaller area sources (e.g., source_id values 36 and 53) have been designed where the seismicity can be assigned to known fault zones.In order to obtain a robust estimation of the necessary parameters for PSHA derived by the statistical analysis of the seismicity, due to the scarcity of data in some of the areas covered by the model, super zones are introduced. These super zones are defined by combining area sources based on similarities in their tectonic regime, and taking into account local expert’s judgments. The super zones are used to estimate: (1) the completeness time of the earthquake catalogue, (2) the depth distribution of seismicity, (3) the tectonic regime through focal mechanisms analysis, (4) the maximum magnitude and (5) the b values via the GR relationship.The earthquake catalogue for focal mechanism is extracted from the Harvard Global Centroid Moment Tensor Catalog (Ekström and Nettles, 2013). For the focal mechanism classification, the Boore et al. (1997) convention is used. This means that an event is considered to be strike-slip if the absolute value of the rake angle is <=30 or >=150 degrees, normal if the rake angle is <-30 or >-150 and reverse (thrust) if the rake angle is >30 or <150 degrees. The distribution of source mechanisms and their weights are estimated for the super zones.For area sources, the maximum magnitude is usually taken from the historical seismicity, but due to some uncertainties in the magnitudes of the largest events, the opinions of the local experts are also included in assigning the maximum magnitude to each super zone. Super zones 2 and 3, which belongs to stable regions, are each assigned a maximum magnitude of 6, after Mooney et al. (2012), which concludes after analyses and observation of modern datasets that at least an event of magnitude 6 can occur anywhere in the world. For hazard calculations, each area source is assigned the maximum magnitude of their respective super zone.For processing the GR parameters (a and b values) for the area sources, the completeness analysis results estimated for the super zones are assigned to the respective smaller area sources. If the individual area source has at least 20 events, the GR parameters are then estimated for the area source. Otherwise, the b value is adopted from the respective super zone to which the smaller area source belongs, and the a value is estimated based on the Weichert (1980) method. This ensures the stability in the b value as well as the variation of activity rate for different sources.The hypocentral depth distribution is estimated from the seismicity inside each super zone. The depth distribution is considered for maximum up to three values. Based on the number of events, the weights are assigned to each distribution. These depth distributions, along with corresponding weights, are further assigned to the area sources within the same super zones.
Distribution file: "EMCA_seismozonesv1.0_shp.zip"Version: v1.0Release date: 2015-07-30Format: ESRI ShapefileGeometry type: polygonsNumber of features: 63Spatial Reference System: +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs Distribution file: "EMCA_seismozonesv1.0_nrml.zip"Version: v1.0Release date: 2015-07-30Format: NRML (XML) Format compatible with the GEM OpenQuake platform (http://www.globalquakemodel.org/openquake/about/platform/) Feature attributes:src_id : Id of the seismic sourcesrc_name : Name of the seismic sourcetect_reg: Tectonic regime of the seismic sourceupp_seismo : Upper level of the the seismogenic depth (km)low_seismo : Lower level of the seismogenic depth (km)mag_scal_r: Magnitude scaling relationshiprup_asp_ra: Rupture aspect ratiomfd_type : Magnitude frequency distribution typemin_mag: Minimum magnitude of the magnitude frequency relationshipmax_mag: Maximum magnitude of the magnitude frequency relationshipa_value: a value of the magnitude frequency relationshipb_balue : b value of the magnitude frequency relationshipnum_npd: number of nodal plane distributionweight_1 : weight of 1st nodal plane distributionstrike_1: Strike of the seismic source (degrees)rake_1: rake of the seismic source (degrees)dip_1: dip of the seismic source (degrees)num_hdd: number of hypocentral depth distributionhdd_d_1: Depth of 1st hypocentral depth distribution (km)hdd_w_1: Weight of 1st hypocentral depth distribution
# 5
Pampel, Heinz
Abstract: These data are supplementary material to Pampel (2019) and present the results of a quantitative survey on Open Access among scientific institutions in Germany. Both the report and the data are available in German only. 701 German universities and research institutions were invited to take part in this survey. From September to November 2018, 403 academic institutions took part. Hence, it is the most comprehensive survey on Open Access practices to this day. The results provide an overview of the current state of policies on Open Access and of the status of Open Access infrastructures in Germany. In addition, the results enable a better understanding of today’s handling and monitoring of Open Access publication costs. Furthermore, the study describes the status of Open Access monitoring and reports on current transformation strategies to promote Open Access. The project was founded by the German Federal Ministry of Education and Research (BMBF) as part of the Project „Options4OA” and conducted by Heinz Pampel of the Helmholtz Open Science Coordination Office. The project was founded by the German Federal Ministry of Education and Research (BMBF) as part of the Project „Options4OA” and conducted by Heinz Pampel of the Helmholtz Open Science Coordination Office. Version history/ Corrigendum(5 Sep 2019) In version 1.0, incorrect percentages were given for questions for which multiple answers were possible. This error was corrected in version 2.0. The following questions were affected: 6, 7, 9, 11, 12, 13, 14, 16, 17, 19, 21, 24, 26, 29, 31, 34 and 38.
# 6
Schleicher, Anja Maria • Jurado, Maria-Jose
Abstract: This data publication uses XRD bulk rock analyses carried out on cuttings aboard D/V Chikyu during the International Ocean Discovery Program (IODP) Expeditions 338 and 348 of the Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) project (Strasser et al, 2014, Tobin et al., 2015). More data on clay minerals in the C0002F and C0002P holes are published by Underwood and Song (2016a and 2016b), and Underwood (2017). These data are supplementary material for Schleicher and Jurado (2019). XRD data of the clay size fraction were analyzed at the University of Michigan, USA, and the GFZ Potsdam, Germany. All XRD analyses of the random powder and texture (oriented) preparation followed the analytical methods described in Moore and Reynolds (1997). Oriented clay size samples were measured under air-dried and glycolated conditions, the latter treatment caused interlayer expansion of swelling clays, allowing the recognition of discrete smectite and mixed-layer smectitic phases. In order to compare the clay mineral content, and the mineral amount relative to the adjacent material, exactly 45 μg of the material was mixed with 1.5 ml deionized water and dropped on a round glass slide (diameter 32 mm). All air-dried samples were measured at a relative humidity (RH) of ~30%, and afterward stored in a desiccator filled with ethylene glycol, in order to investigate the final swelling stage of the smectitic phases. The data are provided as tab delimited table (2019-002_Schleicher-Jurado_XRD-data.txt, see also Table 1 in Schleicher and Jurado, 2019) with the following columns:- Hole: name of the C0002 subhole- Depth (mbsf): depth in meter below surface (mbsf)- Sample (SMW): sample number SMW (solid cuttings taken from drilling mud)- Smectite (int./cps): intensity of smectite in counts per second (cps)- Illite (int./cps): intensity of smectite in counts per second (cps) In addition, the original XRD measurements are provided in raw and text formats (2019-002_Schleicher-Jurado_original-XRD-measurements.zip). All science data from these expeditions are also accesible via the Database of the science data acquired by International Ocean Discovery Program and Integrated Ocean Drilling Program expeditions of D/V Chikyu (http://sio7.jamstec.go.jp/).
# 7
Warsitzka, Michael • Závada, Prokop • Pohlenz, Andre • Rosenau, Matthias
Abstract: This dataset provides friction data from ring-shear tests (RST) for a quartz sand used in analogue experiments at the Institute of Geophysics of the Czech Academy of Science (IGCAS) (Kratinová et al., 2006; Zavada et al., 2009; Lehmann et al., 2017; Krýza et al., 2019). It is characterized by means of internal friction coefficients µ and cohesion C. According to our analysis the materials show a Mohr-Coulomb behaviour characterized by a linear failure envelope. Peak friction coefficients µP of the tested material is ~0.75, dynamic friction coeffi-cients µD is ~0.60 and reactivation friction coefficients µR is ~0.64. Cohesions of the material range between 90 and 130 Pa. The material shows a minor rate-weakening of <1% per ten-fold change in shear velocity v.
# 8
Ge, Zhiyuan • Rosenau, Matthias • Warsitzka, Michael • Rudolf, Michael • Gawthorpe, Robert
Abstract: This data set includes the results of digital image correlation of three experiments on gravitational tectonics at passive margins performed at the Helmholtz Laboratory for Tectonic Modelling (HelTec) of the GFZ German Research Centre for Geosciences in Potsdam in the framework of EPOS transnational access activities in 2018. Detailed descriptions of the experiments and monitoring techniques can be found in Ge et al. (submitted) to which this data set is supplement. The DIC analysis yields quantitative deformation information of the experiment surfaces by means of 3D surface displacements from which strain has been calculated. The data presented here are visualized as surface displacement maps, strain maps and strain evolution maps.
# 9
Polvi, Lina E. • Dietze, Michael • Lotsari, Eliisa • Turowski, Jens M. • Lind, Lovisa
Abstract: The file includes velocity data taken using an acoustic Doppler current profiler (ADCP) (Sontek M9 sensor) (Sontek, 2018) measured in March and June 2018 at the Sävar River, Sweden. The raw data are found in an Excel file and include the longitudinal flow speed (m/s) from each of the measured water depths. We have exported the data from RiverSurveyorLive software (https://www.sontek.com/softwaredetail.php?RiverSurveyor-LIVE-RSL-34#RSL) and cleaned the files to remove extra information, so that they include only the data we used in reported analyses. These velocity profiles were taken within a larger project to examine differences in hydraulics and sediment transport during ice-covered and open channel flow conditions. Within this project, seismic signals of these geomorphic processes were recorded encompassing the velocity measurement periods (Dietze & Polvi 2019). In winter (March 2018), the measurements were taken via holes drilled through the ice. The ‘moving boat’ method was applied in the RiverSurveyorLive software, but the sensor was kept static during the whole ~5-minute long measurement period in each hole. The velocity measurements for each hole are presented in separate Excel sheets in the file. During summer (June 2018), a similar method was ap-plied—the ADCP sensor was kept static for the same length of time in the same locations as the holes. Note that the winter measurements also had ice cover above them. The starting depth was the depth under the ice-water interface during winter, and at the water-air interface during summer. In the file, the velocity measurement cell closest to the surface is in the column “Cell1 Spd”. This column title refers to the speed (i.e., velocity) in m/s, of the corresponding measurement cell number. “Cell1 loc” refers to the depth of the cell from the surface in meters. Similarly, the near-bed layer velocity is in the column “Cell Spd xx,” with the highest number for that measurement location. Each measure-ment time step is found on a new row. If there is #N/A written in the cell, or the cell is empty, it means that there is no data from the corresponding cell.
# 10
Dietze, Elisabeth • Dietze, Michael
Abstract: EMMA – End Member Modelling Analysis of grain-size data is a technique to unmix multimodal grain-size data sets, i.e., to decompose the data into the underlying grain-size distributions (loadings) and their contributions to each sample (scores). The R package EMMAgeo contains a series of functions to perform EMMA based on eigenspace decomposition. The data are rescaled and transformed to receive results in meaningful units, i.e., volume percentage. EMMA can be performed in a deterministic and two robust ways, the latter taking into account incomplete knowledge about model parameters. The model outputs can be interpreted in terms of sediment sources, transport pathways and transport regimes (loadings) as well as their relative importance throughout the sample space (scores).
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