59 documents found in 422ms
# 1
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.
# 2
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.
# 3
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.
# 4
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
# 5
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.
# 6
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.
# 7
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.
# 8
Gaudin, Damien • Cimarelli, Corrado
Abstract: Series of experiments to assess the role of pressure, mass of particles, and grain size distribution in the generation of charges and discharges during shock-tube experiments. Experiments have been achieved between 2017 and 2018 in the facilities of Department of Earth and Environmental Sciences - LMU München.This dataset contains:- an excel spreadsheet summarizing the 63 experiments in the database with their main characteristics- a pdf file for each experiment, with the waveforms of the main instruments used in the experiment (Pressure sensors and Faraday cage) as well as ellaborated data (total amount of charges and discharges, discharge size distribution.
Description of the raw file for each experiment (in CSV format). After the header, the columns display respectively: (1) the time [s](2) the static pressure within the autoclave [MPa](3) the voltage across the Faraday cage [V] on a low-sensitivity channel of the datalogger(4) the voltage across the Faraday cage [V] on a high-sensitivity channel of the datalogger that might saturate in some cases(5) the voltage across the lower antenna [V] as described in Cimarelli et al., 2014 (for some experiments only, otherwise the signal remains close to 0)(6) the voltage across the upper antenna [V] as described in Cimarelli et al., 2014 (for some experiments only, otherwise the signal remains close to 0)(7) the dynamic pressure at the exit of the nozzle [MPa](8) the trigger signal generated by the datalogger [V]
# 9
Dreiling, Jennifer • Tilmann, Frederik
Abstract: BayHunter is an open source Python tool to perform an McMC transdimensional Bayesian inversion of receiver functions and/ or surface wave dispersion. It is inverting for the velocity-depth structure, the number of layers and noise parameters (noise correlation and amplitude). The forward modeling codes are provided within the package, but are easily replaceable with own codes. It is also possible to add (completely different) data sets. The BayWatch module can be used to live-stream the inversion while it is running: this makes it easy to see how each chain is exploring the parameter space, how the data fits and models change and in which direction the inversion progresses.
# 10
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).
spinning wheel Loading next page