RST-Stick-Slipy is a software that analyzes the stick-slip characteristics of granular material tested with the ring shear tester RST.pc01 at the Helmholtz Tectonic Laboratory for Tectonic Modelling (HelTec). This software uses the time series created by the machine to automatically detect slip events and analyses several statistical properties depending on the type of test. Using the detected slip events the reloading stiffness and other properties such as recurrence times and stress drops are calculated. Further statistical properties such as the timing and stress level of slow and fast events are determined. Another segment of the module automatically analyses Slide-Hold-Slide experiments and determines the healing rate and other rate-and-state properties.
These data are supplementary to the GJI research article of Blanke et al. 2020, in which static stress drop estimates of laboratory acoustic emission (AE) waveform records were analyzed. Stick-slip experiments were conducted on two triaxial loaded Westerly Granite samples of different roughness: 1) a smooth saw-cut fault (sample S12) and 2) a rough fault (sample W5). Both experiments resulted in six stick-slip failures of which five were analyzed for each fault. A variant of the spectral ratio technique was applied to find the best fitting source parameters.
Laboratory Experiments:
Acoustic emission waveform data of two triaxial stick-slip experiments was recorded at room temperature on cylindrical oven-dried Westerly Granite samples of 105-107 mm height and 40-50 mm diameter. The experiments were conducted on a smooth saw-cut (sample S12) and a rough fault (sample W5). Both experiments were performed in a servo-controlled MTS loading frame equipped with a pressure vessel. The acoustic emission activity was monitored by 16 piezoceramic transducers with a resonance frequency of about 2 MHz. A transient recording system (DAX-Box, Prökel, Germany) recorded full waveform data in triggered mode at a sampling frequency of 10 MHz and an amplitude resolution of 16 bits. The rough fault W5 was first prepared with Teflon-filled saw-cut notches at 30° inclination to the vertical axis and then fractured at 75 MPa. Then, each sample, S12 and W5, was subjected to constant confining pressure of 133 MPa and 150 MPa and then loaded in axial compression using a strain rate of 3*10-4 mm/s and 3*10-6 mm/s, respectively.
Data description:
The tables 2020-008_Blanke-et-al_S1_S12.txt and 2020-008_Blanke-et-al_S2_W5.txt contain AE locations and occurrence, and source parameter estimates of the smooth fault S12 and the rough fault W5, respectively. Both column headers show coordinates of AE locations (X, Y, Z [mm]), temporal occurrence (t [sec]), seismic moment (M0 [Nm]), corner frequency (f0 [Hz]), source radius (r [mm]), static stress drop (stress drop [MPa]), and moment magnitude (MW). M0 and f0 were estimated from the amplitude spectra, using the spectral ratio technique. The source radii were calculated for S-waves using the dynamic circular source model of Madariaga (1976). Static stress drops were estimated following Eshelby (1957). Both tables are used and displayed in Blanke et al. (2020).
An electric current is induced by the motion of electrical conducting seawater through the ambient geomagnetic field. The periodic oceanic tidal flow induces an electric current that emits periodical time-variable electromagnetic field signals. The radial component of the ocean tide induced magnetic field signals has successfully been extracted from magnetic field observations of the satellite missions CHAMP and Swarm. It is known that the amplitudes of these electromagnetic signals are modulated by, among other influences, variations of the electrical seawater conductivity distribution of the ocean. The electrical seawater conductivity in return depends on seawater temperature and salinity. In order to analyse the influence of variations in oceanic temperature and salinity, we modelled a complete set of monthly time slices of three dimensional global complex amplitudes of these electromagnetic field signals for the years 1990 to 2016. In order to analyse solely the influence of variations in the climate sensitive seawater temperature and salinity on the ocean tide induced magnetic field signals, the influences of the secular variation of the geomagnetic field and temporal variations in ocean tide transports have been neglected.The data set is a supplement to the article of Petereit et al. (2019). The detailed method used to create this data set can be found in the data and methods section of the article and the associated data description file.Several datasets and models have been combined in order to compute the necessary models for the electrical conductivity of the Earth's surface and the ocean tide induced electric currents. These are the two main components needed for the modelling of the electromagnetic field signals that are emitted by the ocean tide induced electric currents.The model for the electrical conductivity of the Earth is composed of three components: a 1-D mantle conductivity distribution (Grayver et al., 2017), the time constant sediment conductivity (Laske & Masters, 1997) and the time-varying ocean conductivity. Ocean conductivity values were derived from a dataset of monthly global seawater temperature and salinity distributions that were derived from in-situ observations (Cabanes et al., 2013) using the TEOS-10 Toolbox (IOC, SCOR & APSO, 2010) to solve the Gibbs-seawater equation.The ocean-tide induced electric current density was computed as the vector product of the oceanic seawater conductivity, the tidal transports of the TPXO8-atlas (Egbert & Erofeeva, 2002) and ambient geomagnetic field of the IGRF-12 (Thébault et al., 2015). While, the oceanic seawater conductivity was variable in time, the tidal transports and the field strength of the ambient geomagnetic field have been kept constant.
Due to poor global data coverage and large data uncertainties, Holocene magnetic field models are of limited resolution. The most commonly used modeling approach is based on an inversion strategy based on truncated spherical harmonics and does not provide uncertainty estimates. Focusing on snapshots of the magnetic field, the CORBASS (CORrelation Based Archeomagnetic SnapShot model) algorithm breaks with the tradition of truncated models by implementing a field model based on Gaussian processes (GP). This way regions covered well by data can be modeled with higher resolution, while areas of poor coverage remain uncertain. The GP approach provides a posterior covariance, together with the (mean) field model, which naturally serves as model uncertainty estimate. A full description of the model can be found in <TODO: reference to our upcoming paper>.CORBASS is provided as a collection of python scripts together with an install file for a conda environment. This way CORBASS can be easily set up under various system dependencies. The gitlab page provides installation instructions, documentation and example notebooks and can be found under "Related Work" in the left panel of this page.A static version of CORBASS is linked under "Files", again in the left panel of this page.
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 DescriptionThe 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.