2019-01-18T07:26:19Zhttp://doidb.wdc-terra.org/oaip/oaioai:doidb.wdc-terra.org:66112018-12-21T14:48:32ZDOIDBDOIDB.GFZ
10.5880/GFZ.2.1.2019.001
Supplementary information to "Deep low-frequency earthquakes reveal ongoing magmatic recharge beneath Laacher See Volcano (Eifel, Germany)": Moment tensor inversion report
Heimann, Sebastian; Dahm, Torsten; Hensch, Martin; Ritter, Joachim; Schmidt, Bernd; Stange, Stefan; Lehmann, Klaus
Supplementary information to "Deep low-frequency earthquakes reveal ongoing magmatic recharge beneath Laacher See Volcano (Eifel, Germany)": Moment tensor inversion report
2019
Potsdam, Germany
GFZ Data Services
http://dx.doi.org/10.5880/GFZ.2.1.2019.001
EARTH SCIENCE
HUMAN DIMENSIONS
NATURAL HAZARDS
EARTHQUAKES
EARTH SCIENCE
HUMAN DIMENSIONS
NATURAL HAZARDS
VOLCANIC ERUPTIONS
EARTH SCIENCE
SOLID EARTH
TECTONICS
VOLCANIC ACTIVITY
geoscientificInformation
geological process > volcanism
geological process > seismic activity > earthquake
low frequency earthquakes
magmatic recharge
crustal magma reservoir
50.32942246771561
50.49700858466151
7.1271304469080405
7.424447951790853
Deutsches GeoForschungsZentrum GFZ
GFZ
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The interactive web page contains supplementary information for a publication by Hensch et al. 2019: "Deep low-frequency earthquakes reveal ongoing magmatic recharge beneath Laacher See Volcano (Eifel, Germany)". Details on the analysis of three tectonic and nine deep low-frequency earthquakes are given, including parameter results, error estimates, and figures. The analysis has been performed using the Grond software package (Heimann et. al 2018).
The open source software for seismic source parameter optimization (Grond, Heimann et al., 2018) implements a bootstrap-based method to retrieve solution sub-spaces, parameter trade-offs and uncertainties of earthquake source parameters. Synthetic and observed P and S phase waveforms are restituted to displacement and filtered between 0.5 and 5 Hz in variable frequency ranges, depending on the signal-to-noise ratio (SNR) and the character of the signals. Station amplification factors and transfer functions have been evaluated before the restitution using an empirical calibration method (see Dahm et al., 2018). From waveforms, different types of body wave attributes were calculated, as amplitude spectra, envelopes, and amplitude spectral ratios.
The Green's functions (GF) were calculated with the orthonormal propagator method (QSEIS, Wang, 1999; see https://github.com/pyrocko/fomosto-qseis/), for a 1 km grid spacing in a volume of 150 km source-receiver distances and 1 - 50 km source depths. The sampling rate was 40 Hz and the GF include near field terms. All GF are stored in a Pyrocko GF store (Pyrocko toolbox, see Heimann et al., 2017). We use a nearest neighbor interpolation in between the grid points of the pre-computed GF.
Restituted observed and synthetic ground displacement time series are filtered and windowed between [-2 s; +3 s] from the expected phase arrival, given the tested candidate source model at each forward modeling step in the optimization. Additional to full waveforms, amplitude spectra, envelopes and spectral ratios between P-SV and SH-SV waves are compared. For spectral ratios, a water level approach was implemented to avoid bias from high noise. All components of the mixed inversion received a proper linear weighting with factors between 0.5 and 3, which was selected after running tests with some master events. Weighting and frequency range were defined different for earthquakes with magnitudes above or below ML 2. P and S phase arrivals have been picked to ensure correct selection of time windows during the centroid inversion, and station blacklists were considered event-wise, depending on the SNR.
The plots show for every event the data fits and different types of solution plots. The naming of pages is self-explanatory, but more information can be found in the Grond documentation (https://pyrocko.org/grond/). In order to evaluate the ensembles of solutions for interpretation, we extended the standard statistical analysis of Grond to consider a cluster analysis of source mechanism distributions before statistical analysis. This is introduced because the best ensemble solutions of many of the DLF events show higher variability and groups of different mechanisms. A simple mean or median does not always represent the families of best performing solutions. We therefore declustered the ensemble of best solutions using the method of Cesca et al. (2013), applying the Kagan angle norm, and performed the statistical analysis for each individual cluster.
DIF
9.8.2