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6626 documents found in 382ms
# 11
Pijnenburg, Ronald • Verberne, Berend • Hangx, Suzanne • Spiers, Christopher
Abstract: Pore pressure reduction in sandstone reservoirs generally leads to small elastic plus inelastic strains. These small strains (0.1 – 1.0% in total) may lead to surface subsidence and induced seismicity. In current geomechanical models, the inelastic component is usually neglected, though its contribution to stress-strain behaviour is poorly constrained. To help bridge this gap, we performed deviatoric and hydrostatic stress-cycling experiments on Slochteren sandstone samples from the seismogenic Groningen gas field in the Netherlands. We explored in-situ conditions of temperature (T = 100°C) and pore fluid chemistry, porosities of 13 to 26% and effective confining pressures (≤ 320 MPa) and differential stresses (≤ 135 MPa) covering and exceeding those relevant to producing fields. The findings of our work are outlined in the corresponding paper. The data presented here are the measured mechanical tabular data and microstructural data (stitched mosaic of backscatter electron images) provided as uncompressed jpg images. In addition, for one sample we include chemical element maps obtained through Electron Dispersive X-ray spectrometry (EDX).
# 12
Isabell Schmidt
Abstract: The dataset provides area-size data on raw-material-polygons - currently for internal use only.
# 13
Kwiatek, Grzegorz • Saarno, Tero • Ader, Thomas • Bluemle, Felix • Bohnhoff, Marco • (et. al.)
Abstract: The dataset is supplementary material to Kwiatek et al. (2019, Science Advances). The dataset is a refined seismic catalog acquired during the hydraulic stimulation of the future geothermal sites located in Espoo, Finland. There, the injection well, OTN-3, was drilled down to 6.1 km-depth into Precambrian crystalline rocks. Well OTN-3 was deviated 45° from vertical and an open hole section at the bottom was divided into several injection intervals. A total of 18,159 m3 of fresh water was pumped into crystal-line rocks during 49 days in June- and July, 2018. The stimulation was monitored in near-real time using (1) a 12-level seismometer array at 2.20-2.65 km depth in an observation well located ~10 m from OTN3 and (2) a 12-station network installed in 0.3-1.15 km deep bore-holes surrounding the project site. On completion of stimulation it the catalog contained 8452 event detections overall, and 6152 confirmed earthquakes located in the vicinity of the project site (epicentral distance from the well head of OTN-3 <5 km). These were recorded in a time period lasting 59 days: 49 days of active stimulation campaign and the 10 days following completion. The initial industrial seismic catalog of 6150 earthquakes was manually reprocessed. The P- and S-wave arrivals of larger seismic events with M>0.5 were all manually verified, and, if necessary, refined. Earthquakes with sufficient number of phases and seemingly anomalous hypocenter depths (e.g. very shallow or very deep) were manually revised as well. The hypocenter locations were calculated using the Equivalent differential time method and optimized with an Adaptive Simulated Annealing algorithm. The updated catalog contained 4,580 earthquakes that occurred at hypocenter depths 4.5-7.0 km, in the vicinity of the stimulation section of OTN-3. To increase the precision of their locations, the selected 2155 earthquakes with at least 10 P-wave and 4 S-wave picks were relocated using the double-difference relocation technique. The relocation uncertainties were estimated using bootstrap resampling technique. The relocation reduced the relative precision of hypocenter determination to approx. 66 m and 27 m for 95% and 68% of relocated earthquakes. The final relocated catalog that constitutes the here published contained 1,977 earthquakes (91% of the originally selected events).
# 14
König, Rolf • Schreiner, Patrick • Dahle, Christoph
Abstract: As a convenience to users who wish to use a replacement value for C(2,0) of GFZ's GRACE/GRACE-FO RL06 GSM products, a monthly GFZ C(2,0) estimate time series is provided. These estimates are obtained from the analysis of Satellite Laser Ranging (SLR) data to the following five geodetic satellites: LAGEOS-1 and 2, Starlette, Stella and Ajisai. Starting from March 2012, the LARES satellite is added so that six geodetic satellites are included. The individual satellites are combined on normal equation level using relative weights which are based on a variance component estimation. Gravity field coefficients up to degree and order 5 plus coefficients C(6,1) and S(6,1) have been simultaneously solved together with all other (non-gravity) parameters. The background models used in the SLR analysis is consistent with the GFZ GRACE/GRACE-FO RL06 processing, including the use of the same Atmosphere-Ocean De-aliasing product AOD1B RL06. IMPORTANT REMARKS: It is advised to use these estimates to replace the C(2,0) values from the GFZ RL06 GSM files. These estimates are not intended to be used with the GRACE RL05 or earlier products. This data set is regularly updated in order to extend the time series on an operational basis. As long as the version number has not changed, all previously available data records have not been changed! See line 'UPDATE HISTORY' in the header of the data file for details about the current time span and version. SPECIAL NOTES: C(2,0) estimates are provided continuously for each month. However, the SLR data was processed in 7-day batches aligned to GPS weeks. Several weekly SLR normal equations were then accumulated to obtain a monthly solution; GPS weeks covering two calendar months were assigned to that calendar month where the majority of days within the week belong to. Thus, the beginning date for these 'monthly' solutions does not necessarily match the first day of a calendar month, but will be within a few days of that corresponding date. Moreover, in most cases, a different number of days was used for the SLR solution than for the corresponding GRACE/GRACE-FO solution. For particular periods, the GRACE/GRACE-FO solutions might span significantly less than one month or cover more than one calendar month. In these cases, a specially dedicated SLR estimate was generated which is based on approximately the same interval so that the epoch of the SLR estimate is close to the epoch of the GRACE/GRACE-FO solution. To distinguish the different cases of C(2,0) estimates mentioned above (monthly vs. specially dedicated) and to easily recognize whether a C(2,0) estimate matches an existing GRACE/GRACE-FO solution, the following flags are appended to each data record:- ' m': C(2,0) estimate represents a monthly solution for a month where no GRACE/ GRACE-FO solution is available.- 'Gm': C(2,0) estimate represents a monthly solution and a corresponding GRACE/ GRACE-FO solution is available.- 'G*': C(2,0) estimate is specially dedicated for a GRACE/GRACE-FO solution as described above; the effective period of data used is additionally provided by a string '<yymmdd>_<YYMMDD>'.
# 15
Dahle, Christoph • Murböck, Michael
Abstract: Post-processed GRACE/GRACE-FO spherical harmonic coefficients of GFZ RL06 Level-2 GSM products representing an estimate of Earth's gravity field variations during the specified timespan. Post-processing steps comprise: (1) subtraction of a long-term mean field; (2) optionally, decorrelation and smoothing with VDK filter (anisotropic filter taking the actual error covariance information of the underlying GSM coefficients into account, see Horvath et al. (2018)); (3) replacement of coefficient C20 and its uncertainty by values estimated from Satellite Laser Ranging (SLR); (4) subtraction of linear trend caused by Glacial Isostatic Adjustment (GIA) as provided by a numerical model; (5) insertion of coefficients of degree 1; and (6) removal of estimated signal with 161 days period. These coefficients represent signals caused by water mass redistribution over the continents and in the oceans. These post-processed GRACE/GRACE-FO GSM products are denoted as Level-2B products. There are multiple variants of Level-2B products available that differ by the characteristics of the anisotropic filter applied. These variants are distinguishable by the following strings in the product file names: - 'NFIL': Level-2B product is not filtered- 'VDK2': Level-2B product is filtered with VDK2- 'VDK3': Level-2B product is filtered with VDK3- 'VDK5': Level-2B product is filtered with VDK5 The individual data sets and models used during the post-processing steps mentioned above are provided as well (in the aux_data folder): - 'GRAVIS-2B_2002095-2016247_GFZOP_0600_NFIL_0001.gz': Long-term mean field calculated as unweighted average of the 156 available GFZ RL06 GSM products in the period from 2002/04 up to and including 2016/08.- 'GFZ_RL06_C20_SLR.dat': C20 time series from SLR (http://doi.org/10.5880/GFZ.GRAVIS_06_C20_SLR)- 'GRAVIS-2B_GIA_ICE5G_VILMA.gz': Model for subtraction of linear trend caused by GIA- 'GRAVIS-2B_DEG1_v01.dat': Degree-1 time series Detailed information about the product is provided in the header of the data file.
# 16
Ziegler, Moritz • Heidbach, Oliver
Abstract: The distribution of data records for the maximum horizontal stress orientation S_Hmax in the Earth’s crust is sparse and very unequally. To analyse the stress pattern and its wavelength and to predict the mean S_Hmax orientation on regular grids, statistical interpolation as conducted e.g. by Coblentz and Richardson (1995), Müller et al. (2003), Heidbach and Höhne (2008), Heidbach et al. (2010) or Reiter et al. (2014) is necessary. Based on their work we wrote the Matlab® script Stress2Grid that provides several features to analyse the mean S_Hmax pattern. The script facilitates and speeds up this analysis and extends the functionality compared to the publications mentioned before. This script is the update of Stress2Grid v1.0 (Ziegler and Heidbach, 2017). It provides two different concepts to calculate the mean S_Hmax orientation on regular grids. The first is using a fixed search radius around the grid points and computes the mean S_Hmax orientation if sufficient data records are within the search radius. The larger the search radius the larger is the filtered wavelength of the stress pattern. The second approach is using variable search radii and determines the search radius for which the standard deviation of the mean S_Hmax orientation is below a given threshold. This approach delivers mean S_Hmax orientations with a user-defined degree of reliability. It resolves local stress perturbations and is not available in areas with conflicting information that result in a large standard deviation. Furthermore, the script can also estimate the deviation between plate motion direction and the mean S_Hmax orientation. The script is fully documented by the accompanying WSM Technical Report 19/02 (Ziegler and Heidbach, 2019) which includes a changelog in the beginning.
# 17
Ziegler, Moritz • Heidbach, Oliver
Abstract: The distribution of data records for the maximum horizontal stress orientation SHmax in the Earth’s crust is sparse and very unequally. In order to analyse the stress pattern and its wavelength or to predict the mean SHmax orientation on a regular grid, statistical interpolation as conducted e.g. by Coblentz and Richardson (1995), Müller et al. (2003), Heidbach and Höhne (2008), Heidbach et al. (2010) or Reiter et al. (2014) is necessary. Based on their work we wrote the Matlab® script Stress2Grid that provides several features to analyse the mean SHmax pattern. The script facilitates and speeds up this analysis and extends the functionality compared to aforementioned publications. The script is complemented by a number of example and input files as described in the WSM Technical Report (Ziegler and Heidbach, 2017, http://doi.org/10.2312/wsm.2017.002). The script provides two different concepts to calculate the mean SHmax orientation on a regular grid. The first is using a fixed search radius around the grid point and computes the mean SHmax orientation if sufficient data records are within the search radius. The larger the search radius the larger is the filtered wavelength of the stress pattern. The second approach is using variable search radii and determines the search radius for which the variance of the mean SHmax orientation is below a given threshold. This approach delivers mean SHmax orientations with a user-defined degree of reliability. It resolves local stress perturbations and is not available in areas with conflicting information that result in a large variance. Furthermore, the script can also estimate the deviation between plate motion direction and the mean SHmax orientation.
# 18
Reyer, Christopher • Chang, Jinfeng • Chen, Min • Forrest, Matt • François, Louis • (et. al.)
Abstract: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for advanced estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate impacts across sectors. ISIMIP2b is the second simulation round of the second phase of ISIMIP. ISIMIP2b considers impacts on different sectors at the global and regional scales: water, fisheries and marine ecosystems, energy supply and demand, forests, biomes, agriculture, agro-economic modeling, terrestrial biodiversity, permafrost, coastal infrastructure, health and lakes. ISIMIP2b simulations focus on separating the impacts and quantifying the pure climate change effects of historical warming (1861-2005) compared to pre-industrial reference levels (1661-1860); and on quantifying the future (2006-2099) and extended future (2006-2299) impact projections accounting for low (RCP2.6), mid-high (RCP6.0) and high (RCP8.5) greenhouse gas emissions, assuming either constant (year 2005) or dynamic population, land and water use and -management, economic development, bioenergy demand, and other societal factors. The scientific rationale for the scenario design is documented in Frieler et al. (2017). The ISIMIP2b bias-corrected observational climate input data (Lange, 2018; Frieler et al., 2017) consists of an updated version of the observational dataset EWEMBI at daily temporal and 0.5° spatial resolution, which better represents the CMIP5 GCM ensemble in terms of both spatial model resolution and equilibrium climate sensitivity. The bias correction methods (Lange, 2018; Frieler et al., 2017; Lange, 2016) were applied to CMIP5 output of GDFL-ESM2M, HadGEM2-ES, IPSL-CM5A-LP and MIROC5. Access to the input data for the impact models, and further information on bias correction methods, is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/isimip2b-bias-correction). This entry refers to the ISIMIP2b simulation data from eight global vegetation (biomes) models:CARAIBCLM4.5,DLEM,LPJmL,ORCHIDEE,VEGAS,VISIT,LPJ-GUESS ----------------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/ (ISIMIP Changelog) and https://www.isimip.org/outputdata/dois-isimip-data-sets/ (ISIMIP DOI publications).----------------------------------------------------------------------------
The ISIMIP2b biomes outputs are based on simulations from 8 global vegetation (biomes) models (see listing) according to the ISIMIP2b protocol (https://www.isimip.org/protocol/#isimip2b). The biomes models simulate biogeochemical processes, biogeography and ecosystem dynamics of natural vegetation and managed lands based on soil, climate and land-use information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/
# 19
van Dongen, Renee • Scherler, Dirk • Wittmann, Hella • von Blanckenburg, Friedhelm
Abstract: Concentrations of in-situ-produced cosmogenic 10Be in river sediment are widely used to estimate catchment-average denudation rates. Typically, the 10Be concentrations are measured in the sand fraction of river sediment. However, the grain size of bedload sediment in most bedrock rivers covers a much wider range. Where 10Be concentrations depend on grain size, denudation rate estimates based on the sand fraction alone are potentially biased. To date, knowledge about catchment attributes that may induce grain-size-dependent 10Be concentrations is incomplete or has only been investigated in modelling studies. Here we present an empirical study on the occurrence of grain-size-dependent 10Be concentrations and the potential controls of hillslope angle, precipitation, lithology, and abrasion. We first conducted a study focusing on the sole effect of precipitation in four granitic catchments located on a climate gradient in the Chilean Coastal Cordillera. We found that observed grain size dependencies of 10Be concentrations in the most-arid and most-humid catchments could be explained by the effect of precipitation on both the scouring depth of erosion processes and the depth of the mixed soil layer. Analysis of a global dataset of published 10Be concentrations in different grain sizes (n=73 catchments) – comprising catchments with contrasting hillslope angles, climate, lithology, and catchment size – revealed a similar pattern. Lower 10Be concentrations in coarse grains (defined as “negative grain size dependency”) emerge frequently in catchments which likely have thin soil and where deep-seated erosion processes (e.g. landslides) excavate grains over a larger depth interval. These catchments include steep (> 25°) and humid catchments (> 2000mm yr-1). Furthermore, we found that an additional cause of negative grain size dependencies may emerge in large catchments with weak lithologies and long sediment travel distances (> 2300–7000 m, depending on lithology) where abrasion may lead to a grain size distribution that is not representative for the entire catchment. The results of this study can be used to evaluate whether catchment-average denudation rates are likely to be biased in particular catchments. Samples from the Chilean Coastal Cordillera were processed in the Helmholtz Laboratory for the Geochemistry of the Earth Surface (HELGES). 10Be/9Be ratios were measured at the University of Cologne and normalized to the KN01-6-2 and KN01-5-3 standards. Denudation rates were calculated using a time-independent scaling scheme according to Lal (1991) and Stone (2002) (St scaling scheme) and the SLHL production rate of 4.01 at g-1 yr-1 as reported by Phillips et al. (2016) The global compilation exists of studies that measured 10Be concentrations in different grain sizes from the same sample location. We only included river basins of <5000 km2 which measured 10Be concentrations in at least one sand-sized fraction <2 mm and at least one coarser fraction >2 mm. Catchment parameters have been recalculated using a 90-m SRTM DEM. The data are presented in Excel and csv tables. Table S1 describes the characteristics of the samples catchments, Table S2 includes the grain size dependent 10Be-concentrations measured during this study and Table 3 the global compilation of grain size dependent 10Be-concentrations. All samples of this study (the Chilean Coastal Cordillera) are assigned with International Geo Sample Numbers (IGSN). The IGSN links are included in Table S2 and in the Related References Section on the DOI Landing Page. The data are described in detail in the data description file and in van Dongen et al. (2018) to which they are supplementary material to.
# 20
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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