Acoustic emission source parameters of laboratory triaxial stick-slip experiments on two Westerly granite samples
Cite as:
Blanke, Aglaja; Goebel, Thomas; Kwiatek, Grzegorz (2020): Acoustic emission source parameters of laboratory triaxial stick-slip experiments on two Westerly granite samples. GFZ Data Services. https://doi.org/10.5880/GFZ.4.2.2020.008
Status
I N R E V I E W : Blanke, Aglaja; Goebel, Thomas; Kwiatek, Grzegorz (2020): Acoustic emission source parameters of laboratory triaxial stick-slip experiments on two Westerly granite samples. GFZ Data Services. https://doi.org/10.5880/GFZ.4.2.2020.008
Abstract
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).
Methods
A variant of the spectral ratio technique (e.g. Kwiatek et al., 2011; 2014) was applied to estimate source parameters, e.g. corner frequency, seismic moment, source size and relative static stress drop. The point-source model of Boatwright (1978) was used to fit the spectra. First, AE events were grouped to large AE event populations according to stick-slip cycles. Then, spectral ratios were computed for each group based on linked AE event pairs. Event pairs had to meet quality aspects (cf. Blanke et al., 2020) to maximize the empirical Green’s function (eGf) criteria. The linking of several AE events allowed to have multi-eGfs for each single AE event. A Simulated Annealing approach based on non-stationary Metropolis-Hastings Random Walk algorithm was applied to solve the multidimensional inversion problem.
Authors
Blanke, Aglaja;GFZ German Research Centre for Geosciences, Potsdam, Germany
Goebel, Thomas;Center for Earthquake Research & Information, University of Memphis, Memphis, US
Kwiatek, Grzegorz;GFZ German Research Centre for Geosciences, Potsdam, Germany
Contact
Blanke, Aglaja
(Scientific Researcher); GFZ German Research Centre for Geosciences, Potsdam, Germany;
Keywords
seismology, earthquake source, laboratory acoustic emission, static stress drop, source parameters, spectral ratio, Westerly granite, triaxial, stick-slip, laboratory experiment, stick-slip failure, scaling relations, stress drop dependence, slip-controlled, scale-invariant, scale-dependent, multi-scale, cross-scale, scaling breakdown, mechanical stress drop, multi-empirical Green's functions, magnitude, seismic moment, corner frequency, source radius, EPOS, European Plate Observing System, multi-scale laboratories, rock and melt physical properties, analysis > data analysis, experiment > laboratory experiment, research > scientific research > laboratory research
affiliation (affiliationIdentifier=0000-0001-5252-965X affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
affiliation (affiliationIdentifier=0000-0003-1076-615X affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
titles
title: Acoustic emission source parameters of laboratory triaxial stick-slip experiments on two Westerly granite samples
publisher: GFZ Data Services
publicationYear: 2020
subjects
subject: seismology
subject: earthquake source
subject: laboratory acoustic emission
subject: static stress drop
subject: source parameters
subject: spectral ratio
subject: Westerly granite
subject: triaxial
subject: stick-slip
subject: laboratory experiment
subject: stick-slip failure
subject: scaling relations
subject: stress drop dependence
subject: slip-controlled
subject: scale-invariant
subject: scale-dependent
subject: multi-scale
subject: cross-scale
subject: scaling breakdown
subject: mechanical stress drop
subject: multi-empirical Green's functions
subject: magnitude
subject: seismic moment
subject: corner frequency
subject: source radius
subject: EPOS
subject: European Plate Observing System
subject: multi-scale laboratories
subject: rock and melt physical properties
subject (subjectScheme=GEMET - INSPIRE themes, version 1.0): analysis > data analysis
affiliation (affiliationIdentifier=0000-0001-5252-965X affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
contributor (contributorType=Supervisor)
contributorName (nameType=Personal): Kwiatek, Grzegorz
affiliation (affiliationIdentifier=0000-0003-1076-615X affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
CharacterString: 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).
pointOfContact
CI_ResponsibleParty
individualName
CharacterString: Blanke, Aglaja
organisationName
CharacterString: GFZ German Research Centre for Geosciences, Potsdam, Germany
CI_OnLineFunctionCode (codeList=http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_OnLineFunctionCode codeListValue=http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_OnLineFunctionCode_information): information
CharacterString: A variant of the spectral ratio technique (e.g. Kwiatek et al., 2011; 2014) was applied to estimate source parameters, e.g. corner frequency, seismic moment, source size and relative static stress drop. The point-source model of Boatwright (1978) was used to fit the spectra. First, AE events were grouped to large AE event populations according to stick-slip cycles. Then, spectral ratios were computed for each group based on linked AE event pairs. Event pairs had to meet quality aspects (cf. Blanke et al., 2020) to maximize the empirical Green’s function (eGf) criteria. The linking of several AE events allowed to have multi-eGfs for each single AE event. A Simulated Annealing approach based on non-stationary Metropolis-Hastings Random Walk algorithm was applied to solve the multidimensional inversion problem.
Released
Dataset
Abstract
;GFZ German Research Centre for Geosciences, Potsdam, Germany
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