EGU2020-16191, updated on 12 Jun 2020
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Towards field data applications of six-component polarization analysis

David Sollberger1, Heiner Igel2, Cedric Schmelzbach1, Felix Bernauer2, Shihao Yuan2, Joachim Wassermann2, André Gebauer3, Ulrich Schreiber3, and Johan Robertsson1
David Sollberger et al.
  • 1ETH Zürich, Institute of Geophysics, Zürich, Switzerland (
  • 2LMU München, Munich, Germany
  • 3TUM, Munich, Germany

The analysis of the relative amplitudes of a passing seismic wave recorded on a single seismometer measuring six degrees of freedom of ground motion (translation and rotation) theoretically allows one to extract information on the wave that can conventionally only be obtained from receiver arrays. In the past, it has been shown on numerical data that the extension of conventional three-component (3C) polarization analysis techniques to six-components, allows one to unambiguously identify the wave type of a passing wave and characterise it in terms of its propagation direction (without the 180° ambiguity inherent in 3C data) and local wave speed. Additionally, due to the increase in the dimensionality of the data, two waves arriving at a station at the same time can be simultaneously characterised under ideal conditions (low noise).

Attempts to apply such 6-C polarization analysis techniques to field data have so far been met with limited success. Varying noise levels on the individual components and complex wavefields (with more than two interfering waves arriving at the station at the same time) usually prevent the stable recovery of wave parameters using single-station 6-C polarization analysis.

Here we discuss first attempts to overcome these issues. We (1) test the robustness of different wave parameter estimators (maximum likelihood, MUSIC) towards high levels of noise and (2) we try to reduce the number of interfering events in the analysis window by performing 6-C polarization analysis on time-frequency decomposed seismograms (i.e. spectrograms) using the S-transform.

The new techniques are extensively tested on field data recorded on the high-performance ROMY ringlaser.

How to cite: Sollberger, D., Igel, H., Schmelzbach, C., Bernauer, F., Yuan, S., Wassermann, J., Gebauer, A., Schreiber, U., and Robertsson, J.: Towards field data applications of six-component polarization analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16191,, 2020

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Presentation version 1 – uploaded on 05 May 2020
  • CC1: Comment on EGU2020-16191, Eva Eibl, 06 May 2020

    Hi David,

    nice display. You mentioned that you ran into "the lower bound of the prior". What do you mean by that exactly? Is this the edge of the space you search?  You also mention that a wider search range would make the result more stable. It this a difficult or time consuming task?



    P.s. I have a rotational display in the volcanoseismology session (Thur. 16:00) that might be interesting to you.

  • AC1: Comment on EGU2020-16191, David Sollberger, 06 May 2020

    Hi Eva, 

    Yes exactly, I chose a too narrow search space for the Rayleigh wave velocities. The velocities at high frequencies probably drop below 3000 m/s (the lower bound of the tested parameters). As you can imagine, the shown analysis is computationally very expensive, so I did not manage to get new results on time.