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# 11
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.
# 12
Schimmel, Mariska • Hangx, Suzanne • Spiers, Chris
Abstract: We studied the effect of pore fluid chemistry on compaction creep in quartz sand aggregates, as an analogue for clean, highly porous, quartz-rich reservoir sands and sandstone. Creep is specifically addressed, because it is not yet well understood and can potentially cause reservoir compaction even after production has ceased. Going beyond previous work, we focused on fluids typically considered for pressure maintenance or for permanent storage, e.g. water, wastewater, CO2 and N2, as well as agents, such as AlCl3, a quartz dissolution inhibitor, and scaling inhibitors used in water treatment facilities and geothermal energy production. Uniaxial (oedometer) compaction experiments were performed on cylindrical sand samples at constant effective stress (35 MPa) and constant temperature (80 °C), simulating typical reservoir depths of 2-4 km. Insight into the deformation mechanisms operating at the grain scale was obtained via acoustic emission (AE) counting, and by means of microstructural study and grain size analysis applied before and after individual compaction tests.
Data logging and output:The present data was obtained using an Instron loading frame employed with a uniaxial (oedometer) compaction vessel located in the HPT laboratory at Utrecht University. A complete description of the machine is provided by Schimmel et al., (2019). Mechanical and acoustic emission (AE) data were recorded at 1 Hz using National Instrument (NI) VI Logger software, an overview is presented in Table 1. Table 1. Overview of recorded data Name Unit Description Row - - Instron load V Load externally measured by the Instron loading frame Instron position V Position of the Instron loading ramp measured by the Instron LVDT Local load V Load internally measured by the local load cell Local position V Position of the top measured by the local LVDT Temperature V Sample temperature measured close to the sample Count A - Number of AE counts from counter A Count B - Number of AE counts from counter B Data processingAll measured quantities were converted to realistic units using the following conversions: - Time [s] = row * 1 - Instron load [kN]= Instron load [V] * 10 - Instron position [mm] = Instron position [V] * 5 - Local load [kN] = local load [V] * 33.3 - Local position [mm]= local position [V] * -0.100684133 - Temperature [°C] = temperature [V] * 100 The displacement data were calculated from the Instron and local position, which were corrected for apparatus distortion and thermal expansion using calibrations carried out in an empty vessel at pressure and temperature conditions covering the present experiments. The displacement data (D) were corrected according to Dsample = Dtotal – D¬distortion Where D¬distortion = 1.126e-09 * x8 - 7.744e-08 * x7 + 2.059e-06 * x6 - 2.5e-05 * x5 + 9.109e-05 * x4 + 0.0009916 * x3 - 0.01238 * x2 + 0.066 * x And is x is the applied load (Instron load). Microstructural dataGrain size analysis was performed on one undeformed and several deformed samples using a Malvern laser diffraction particle sizer. This allowed determination of the average grain size and grain size distribution before and after deformation. Laser particle size analysis systematically overestimates grain size by approximately 25 %, due to fines adhering to coarse grains. Stitched micrographs are given for one sample that was only pre-compacted and several samples that were allowed to creep after pre-compaction. Portions of these micrographs were used for crack density analysis.
# 13
Ziegler, Moritz O. • Ziebarth, Malte • Reiter, Karsten
Abstract: In geosciences the discretization of complex 3D model volumes into finite elements can be a time-consuming task and often needs experience with a professional software. Especially outcropping or out-pinching geological units, i.e. geological layers that are represented in the model volume, pose serious challenges. Changes in the geometry of a model may occur well into a project at a point, when re-meshing is not an option anymore or would involve a significant amount of additional time to invest. In order to speed up and automate the process of discretization, Apple PY (Automatic Portioning Preventing Lengthy manual Element assignment for PYthon) separates the process of mesh-generation and unit assignment. It requires an existing uniform mesh together with separate information on the depths of the interfaces between geological units (herein called horizons). These two pieces of information are combined and used to assign the individual elements to different units. The uniform mesh is created with a standard meshing software and contains no or only very few and simple structures. The mesh has to be available as an Abaqus input file. The information on the horizons depths and lateral variations in the depths is provided in a text file. Apple PY compares the element location and depth with that of the horizons in order to assign each element to a corresponding geological unit below or above a certain horizon. Version History: Version 1.01 (29 August 2019) : Bug fixes - no change in functionality Manual for Version 1.0 remains valid - elems_exclude works now as designed and described in the manual.- commenting out elems_exclude does not crash the script anymore.- create_horizon_file does not create two instances of the uppermost horizon.
# 14
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.
# 15
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 ( 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.
# 16
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).
# 17
Sili, Giulia • Urbani, Stefano • Acocella, Valerio
Abstract: This data publication includes movies and figures of twenty-six analogue models which are used to investigate what controls sill emplacement, defining a hierarchy among a selection of the proposed factors: compressive stresses, interface strength between layers, rigidity contrast between layers, density layering, ratio of layer thickness, magma flow rate and driving buoyancy pressure (Sili et al., 2019). Crust layering is simulated by pig-skin gelatin layers and magma intrusions is simulated by colored water. The experimental set-up is composed of a 40.5 X 29 X 40 cm3 clear-Perspex tank where a mobile wall applies a deviatoric compressive stress (C, in Table 1) to the solid gelatin (Figure 1). In each experiment is imposed two layers with different density and rigidity, separated by a weak or strong interface, excluding two experiments characterized by homogeneous gelatin (experiment 4 and 12). Three different rigidity contrast (1, 1.3, 1.8) between the two layers are imposed, defined as the ratio between the Young’s moduli of the upper (Eu) and lower (El) layer. By using NaCl and gelatin concentration, two layers with same rigidity but different densities are obtained, investigating the influence of the density contrasts on sill emplacement. The effects of the ratio between layer thicknesses (i.e. the ratio between upper and lower layer thickness: Thu/Thl) was simulated by changing only the thickness of the upper layer; while magma flow rate are studied changing the flow rate of peristaltic pump. Water density was increased by adding NaCl to analyze the effect of changing driving buoyancy pressure (Pm) that depends on the density difference between host rock and magma (Δρ), gravitational acceleration (g) and intrusion length (H). In the table different colors indicate the experiment result: black = dike; red = sill and blue = sheet. The here provided material includes time-lapse movies showing intrusion propagation of the twenty-six models with a velocity of 5 times higher compared to the real time (1 second in the movie is 25 real seconds). These visualizations are side (XZ or YZ plane in Figure 1) and/or top views (XY plane in Figure 1).
# 18
Zingerle, Philipp • Brockmann, Jan Martin • Pail, Roland • Gruber, Thomas • Willberg, Martin
Abstract: TIM_R6e is an extended version of the satellity-only global gravity field model TIM_R6 (Brockmann et al., 2019) which includes additional terrestrial gravity field observations over GOCE's polar gap areas. The included terrestrial information consists of the PolarGap campaign data (Forsberg et al., 2017) augumented by the AntGG gravity data compilation (Scheinert et al., 2016) over the southern polar gap (>83°S) and the ArcGP data (Forsberg et al. 2007) over the northern polar gap (>83°N). The combination is performed on normal equation level, encompassing the terrestrial data as spectrally limited geographic 0.5°x0.5° grids over the polar gaps.
Processing procedures: (extending TIM_R6) Gravity from orbits (SST): (identical to TIM_R6)- short-arc integral method applied to kinematic orbits, up to degree/order 150- orbit variance information included as part of the stochastic model, it is refined by empirical covariance functions Gravity from gradients (SGG): (identical to TIM_R6)- parameterization up to degree/order 300- observations used: Vxx, Vyy, Vzz and Vxz in the Gradiometer Reference Frame (GRF)- realistic stochastic modelling by applying digital decorrelation filters to the observation equations; estimated separately for individual data segments applying a robust procedure Gravity from terrestrial observations (TER):- collocation of the original terrestrial data sources onto 30'x30' geographic gravity disturbance grids (in the polar gap areas above 83° southern/northern latitude, thus forming a pair of polar caps)- spectral limitation of the data to D/O 300 within the collocation process- the chosen grid is fully compatible with the grid of the zero observation constraints of the original TIM_R6 model. In its function it replaces the original constraints- from the collocated polar caps, a partial normal equation system, up to D/O 300 is derived Combined solution:- addition of normal equations (SST D/O 150, SGG D/O 300, TER D/O 300)- Constraints: * Kaula-regularization applied to coefficients of degrees/orders 201 - 300 (constrained towards zero, fully compatible with TIM_R6)- weighting of SST and SGG is identical to TIM_R6. All TER observations are weighted with 5 mGal. Specific features of resulting gravity field: - Gravity field solution is (mostly) independent of any other gravity field information (outside the polar gap region)- Constraint towards zero starting from degree/order 201 to improve signal-to-noise ratio- Related variance-covariance information represents very well the true errors of the coefficients (outside the polar gap region)- Solution can be used for independent comparison and combination on normal equation level with other satellite-only models (e.g. GRACE), terrestrial gravity data, and altimetry (outside the polar gap region)- Since in the low degrees the solution is based solely on GOCE orbits, it is not competitive with a GRACE model in this spectral region (outside the polar gap region)- In comparison to TIM_R6, TIM_R6e should deliver more accurate results, especially towards the polar gaps. However, as it uses additional data sources it cannot be seen as totally independent anymore: even outside the polar gap regions correlations (introduced by the holistic nature of spherical harmonics) may be found.
# 19
Hyde, Emilee • Kyba, Christopher
Abstract: This dataset is related to the question of whether communities inside of certified "International Dark Sky Places" have different levels of lighting change in comparison to communities of similar size that are located further away. It is a supplement to Coesfeld et al. (2019), in which this question was examined for the time period 2012-2018. This dataset contains the boundaries of the analysis areas (i.e. community boundaries) in the directory "Coordinates and Graphs". These boundaries are stored as polygons in plain text format. Additional data related to the publication (e.g. Excel tables containing summary data of measured lighting trends for each community) are also included. Details of the data are available in the data description file.
# 20
Lühr, Birger • Ibanez, Jesus M. • Dahm, Torsten
Abstract: The TOMO-ETNA experiment was focused on the base of generation and acquisition of seismic signal (active and passive) at Mt. Etna volcano and surrounding area. The terrestrial campaign consists in the deployment of 80 short-period three-component seismic stations (June 15 to July24), 17 Broadband seismometers (June 15 to October 30) provided by Helmholtz Centre Potsdam (GFZ) German Research Centre for Geosciences using the German Geophysical Instrument Pool Potsdam (GIPP Gerätepool Geophysik), and the coordination with 133 permanent seismic station belonging to the “Istituto Nazionale di Geofisica e Vulcanologia” (INGV) of Italy. This temporary seismic network recorded active and passive seismic sources. Active seismic sources were generated by an array of air-guns mounted in the Spanish Oceanographic vessel “Sarmiento de Gamboa” with a power capacity of up to 5.200 cubic inches. In total more than 26.000 shots were fired and more than 450 local and regional earthquakes were recorded. Until July the Oceanographic Vessel “Sarmiento de Gamboa” and the hydrographic vessel “Galatea” were responsible for the offshore activities, that included deployment of OBSs, and several marine activities. The vessel “Aegaeo” performed additional seismic, magnetic and gravimetric experiments until the end of November 2014. This experiment was part of the “Task 5.3 - Mt. Etna structure” of the “EU MED-SUV Project” concerned with the investigation of Mt. Etna volcano (seismic tomography experiment - TOMO-ETNA) by means of passive and active refraction/reflection seismic methods. It focused on the investigation of Etna’s roots and surrounding areas by means of passive and active seismic methods. Therefore, this experiment included activities both on-land and offshore with the main objective to obtain a new high-resolution tomography in order to improve the 3D image of the crustal structures existing beneath the Etna volcano and the northeast Sicily (Peloritani - Nebrodi chain) up to the Aeolian Islands. Waveform data are open and available from the GEOFON data centre, under network code 1T.
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