391 documents found in 499ms
# 1
Ziegler, Moritz O.
Abstract: The 3D geomechanical-numerical modelling of the in-situ stress state requires observed stress information at reference locations within the model area to be compared to the modelled stress state. This comparison of stress states and the ensuing adaptation of the displacement boundary conditions provide a best fit stress state in the entire model region that is based on the available stress information. This process is also referred to as calibration. Depending on the amount of available information and the complexity of the model the calibration is a lengthy process of trial-and-error modelling and analysis. The Fast Automatic Stress Tensor Calibration (FAST Calibration) is a method and a Matlab script that facilitates and speeds up the calibration process that has been developed in the framework of the World Stress Map (WSM, Heidbach et al., 2010; 2016). The method requires only three model scenarios with different boundary conditions. The modelled stress states at the locations of the observed stress state are extracted. Then they are used to compute the displacement boundary conditions that are required in order to achieve the best fit of the modelled to the observed stress state. Furthermore, the influence of the individual observed stress information on the resulting stress state can be weighted. The FAST-Calibration (Fast Automatic Stress Tensor Calibration) is a Matlab tool that controls the statistical calibration of a 3D geomechanical-numerical model of the stress state following the approach described by Reiter and Heidbach (2014), Hergert et al. (2015), and Ziegler et al. (2016). It is mainly designed to support the multi-stage modelling procedure presented by Ziegler et al. (2016). However, it can also be used for the calibration of a single-stage model. The tools run in Matlab 2017a and higher and are meant to work with the visualization software Tecplot 360 EX 2015 R2 and higher (https://www.tecplot.com/products/tecplot-360/) in conjunction with the Tecplot 360 Add-on GeoStress (Stromeyer and Heidbach, 2017). The user should be familiar with 3D geomechanical-numerical modelling, Matlab, Tecplot 360 EX, including a basic knowledge of Tecplot 360 EX macro functions, and the Tecplot 360 EX Add-on GeoStress. This FAST Calibration manual provides an overview of the scripts and is designed to help the user to adapt the scripts for their own needs.
# 2
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.
# 3
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.
# 4
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.
# 5
Pick, Leonie • Korte, Monika
Abstract: The HMC (Hourly Magnetospheric Currents) index measures the activity of large-scale magnetospheric currents on Earth's surface from 1900 to 2015. It resolves the absolute intensity of low-frequency variations, especially at periods relevant to the solar cycle, more robustly than existing geomagnetic indices. HMC is based on hourly means of vector magnetic field measurements from 34 mid latitude geomagnetic observatories obtained from WDC Edinburgh (http://www.wdc.bgs.ac.uk/catalog/master.html). This data has been manually revised to correct for spikes, jumps and drifts. A detailed description of the derivation method is given in Pick et al., 2018 to which these data are supplementary material. This directory contains the HMC index (hmc1900phor.hor) and the modified observatory data that it is based on (data.zip). The index and the observatory data files are formatted in compliance with the IAGA-2002 ASCII exchange format (https://www.ngdc.noaa.gov/IAGA/vdat/IAGA2002/iaga2002format.html). Individual file names are composed of:[IAGA code of observatory] + [first active year during 1900-2015] + [p(provisional)] + [hor(hourly)] + [_mod(modified)].hor Also included is information on how the data modifications (list in modifications.pdf) were applied (readme.txt).
# 6
Steed, Robert • Fuenzalida, Amaya
Abstract: This archive contains datasets pertaining to the article "Crowdsourcing triggers rapid, reliable earthquake locations" by Steed et al. (2018). There is a dataset containing the European-Mediterranean Seismological Centre's detections of seismological events via crowdsourced methods (i.e. monitoring of internet traffic on the site www.emsc-csem.org, usage of the EMSC app LastQuake or monitoring of tweets containing earthquake related words). This dataset covers the years 2016 and 2017 and contains 2590 detections. The other dataset contains the raw results from testing the CsLoc system (Crowdseeded seismic Location) on the historical data of 2016 and 2017; this system is described in the article for which this dataset is supplemental material. This dataset was used for the creation of the results presented in the article. The archive contains more detailed descriptions of the datasets, which are stored in csv files, including the definition of column heads (*_dataset_description.csv). List of files:2018-068_Steed-et-al_README.txtcrowdsourced_detections_dataset.csvcrowdsourced_detections_dataset_descriptions.csvcrowdsourced_detections_auditting.txtCsLoc_publication_dataset.csvCsLoc_publication_dataset_descriptions.csv
# 7
GEOFON Data Centre
Abstract: P-phase arrival times automatically created by the SeisComP3 (https://www.seiscomp3.org/) software at the GFZ scanning all stations available in real-time at GEOFON Data Centre and listed in the contributors list. Data have been used in the publication by Steed et al 2018 to test the new CsLoc method, sent in relatime to EMSC using the HMB application (Heinloo, 2016, http://doi.org/10.5880/GFZ.2.4.2016.001). The data sets includes P-phases from 2016 and 2017. The data are presented in two csv tables (part I and part II) that are included in the folder 2018-002_Geofon_csloc_test_phases.zip.
# 8
Corbi, Fabio • Sandri, Laura • Bedford, Jonathan • Funiciello, Francesca • Brizzi, Silvia • (et. al.)
Abstract: This data set includes the results of digital image correlation of one experiment on subduction megathrust earthquakes with interacting asperities performed at the Laboratory of Experimental Tectonics (LET) Univ. Roma Tre in the framework of AspSync, the Marie Curie project (grant agreement 658034) lead by F. Corbi in 2016-2017. Detailed descriptions of the experiments and monitoring techniques can be found in Corbi et al. (2017 and 2019) to which this data set is supplementary material. We here provide Digital Image Correlation (DIC) data relative to a 7 min long interval during which the experiment 
produces 40 seismic cycles with average duration of about 10.5 s (see Figure S1 in Corbi et al., 2019). The DIC analysis yields quantitative about the velocity field characterizing two consecutive frames, measured in this case at the model surface. For a detailed description of the experimental procedure, set-up and materials used, please refer to the article of Corbi et al. (2017) paragraph 2. This data set has been used for: a) studying the correlation between apparent slip-deficit maps and earthquake slip pattern (see Corbi et al., 2019; paragraph 4); and b) as input for the Machine Learning investigation (see Corbi et al., 2019; paragraph 5). Further technical information about the methods, data products and matlab scripts is proviced in the data description file. The list of files explains the file and folder structure of the data set.
# 9
Nooshiri, Nima • Heimann, Sebastian • Tilmann, Frederik • Dahm, Torsten • Saul, Joachim
Abstract: We present SCOTER, an open-source Python programming package that is designed to relocate multiple seismic events by using direct P- and S-wave station correction terms. The package implements static and shrinking-box source-specific station terms techniques extended to regional and teleseimic distances and adopted for probabilistic, non-linear, global-search location for large-scale multiple-event location. This program provides robust relocation results for seismic event sequences over a wide range of spatial and temporal scales by applying empirical corrections for the biasing effects of 3-D velocity structure. Written in the Python programming language, SCOTER is run as a stand-alone command-line tool (requiring no knowledge of Python) and also provides a set of sub-commands to develop required input files (e.g. phase files, travel-time grid files, configuration) and export relocation results (such as hypocenter parameters, travel-time residuals) in different formats -- routine but non-trivial tasks that can consume much user time. This package can be used for relocating data sets in local, regional, and teleseimic scales.
# 10
Förster, Hans-Jürgen
Abstract: This data set is the 4th contribution of a series reporting chemical data for accessory minerals from felsic igneous rocks. It deals with two late Variscan biotite-granite massifs emplaced in the Saxothuringian Zone of the Variscan Orogen (Erzgebirge−Vogtland metallogenic province) in Germany. Mineral compositions were measured by electron-microprobe on surface rocks and borehole samples. The data set assembles the results of electron-microprobe spot analyses of primary and secondary allanite-(Ce), monazite-(Ce), xenotime-(Y) and zircon from the multi-phase biotite-granite plutons of Kirchberg (KIB, Western Erzgebirge) and Niederbobritzsch (NBZ, Eastern Erzgebirge). Both plutons comprise several, compositionally and texturally distinct sub-intrusions, contain locally centimeter- to decimeter-sized co-genetic enclaves and xenoliths, and are cross-cut by chemically distinct, fine-grained aplitic dikes. These late-Variscan (c. 325 Ma) granites are moderately to highly evolved and (not considering enclaves) span the SiO2-range (in wt%) 67.0-77.4 (KIB) and 66.8-76.2 (NBZ). The granites are weakly peraluminous (A/CNK = 1.04−1.11 for KIB and 0.99-1.10 for NBZ) and of transitional I−S-type affinity. Formation of primary allanite-(Ce) was restricted to the least-evolved subintrusions KIB1 and NBZ1 of both massifs. All other granites contain monazite-(Ce) as predominant LREE host. Magmatic allanite-(Ce) is variably altered and characterized by totals <100 wt%, implying the presence of several wt% water in the structure. Synchysite-(Ce) constitutes one of its alteration minerals. The Kirchberg massif hosts a second sub-facies of KIB1 that contains monazite instead of allanite as primary species. Severe alteration of this granite facies gave rise to partial or complete dissolution of part of the monazite accompanied by formation of allanite-epidote solid solutions as alteration product. Monazite-(Ce) displays large variations in Th versus REE concentrations even at thin-section scale. Incorporation of Th is mainly governed by the huttonite substitution Th^4+ + Si^4+ = REE^3+ + P^5+. Thorium concentrations span the range 1.33 – 41.8 wt.% ThO2. Xenotime-(Y) does not occur in KBI1 and NBZ1, but crystallized in all other subintrusions. Notable is the predominance of the heaviest REE Er-Lu (normalized to chondrite). The data set contains the complete pile of electron-microprobe analyses for the four accessory minerals allanite-(Ce) (ALLA-KIB-NBZ2019), monazite-(Ce) (MONA-KIB-NBZ2019), xenotime-(Y) (XENO-KIB-NBZ2019) and zircon (ZIRC-KIB-NBZ2019). All tables are presented as Excel (xlsx) and machine-readable csv formats. The content of the tables and further data description are given in the data description file, together with BSE images of primary and secondary allanite-(Ce) from the KIB1 subintrusion.
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