EGU General Assembly 2020
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Near-field spectral analysis of data-integrative dynamic rupture earthquake simulations of the 1992 Landers earthquake

Nico Schliwa and Alice-Agnes Gabriel
Nico Schliwa and Alice-Agnes Gabriel
  • Ludwig-Maximilians-Universität (LMU) München, Earth and Environmental Sciences, Germany (

The rise of observations from Distributed Acoustic Sensing (e.g., Zhan 2020) and high-rate GNSS networks (e.g., Madariaga et al., 2019) highlight the potential of dense ground motion observations in the near-field of large earthquakes. Here, spectral analysis of >100,000 synthetic near-field strong motion waveforms (up to 2 Hz) is presented in terms of directivity, corner frequency, fall-off rate, moment estimates and static displacements.

The waveforms are generated in 3‐D large-scale dynamic rupture simulations which incorporate the interplay of complex fault geometry, topography, 3‐D rheology and viscoelastic attenuation (Wollherr et al., 2019). A preferred scenario accounts for off-fault deformation and reproduces a broad range of observations, including final slip distribution, shallow slip deficits, and spontaneous rupture termination and transfers between fault segments. We examine the effects of variations in modeling parameterization within a suite of scenarios including purely elastic setups and models neglecting viscoelastic attenuation. 

First, near-field corner frequency mapping implementing a novel spectral seismological misfit criterion reveals rays of elevated corner frequencies radiating from each slipping fault at 45 degree to rupture forward direction. The azimuthal spectral variations are specifically dominant in the vertical components indicating we map rays of direct P-waves prevailing (Hanks, 1980). The spatial variation in corner frequencies carries information on co-seismic fault segmentation, slip distribution, focal mechanisms and stress drop. Second, spectral fall-off rates are variably inferred during picking the associated corner frequencies to identify the crossover from near-field to far-field spectral behaviour in dependence on distance and azimuth. Third, we determine static displacements with the help of near-field seismic spectra.

Our findings highlight the future potential of spectral analysis of spatially dense (low frequency) ground motion observations for inferring earthquake kinematics and understanding earthquake physics directly from near-field data; while synthetic studies are crucial to identify "what to look for" in the vast amount of data generated.


Hanks, T.C., 1980. The corner frequency shift, earthquake source models and Q.

Madariaga, R., Ruiz, S., Rivera, E., Leyton, F. and Baez, J.C., 2019. Near-field spectra of large earthquakes. Pure and Applied Geophysics, 176(3), pp.983-1001.

Wollherr, S., Gabriel, A.-A. and Mai, P.M., 2019.  Landers 1992 “reloaded”: Integrative dynamic earthquake rupture modeling. Journal of Geophysical Research: Solid Earth, 124(7), pp.6666-6702.

Zhan, Z., 2020. Distributed Acoustic Sensing Turns Fiber‐Optic Cables into Sensitive Seismic Antennas. Seismological Research Letters, 91(1), pp.1-15.

How to cite: Schliwa, N. and Gabriel, A.-A.: Near-field spectral analysis of data-integrative dynamic rupture earthquake simulations of the 1992 Landers earthquake , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22673,, 2020

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Presentation version 1 – uploaded on 01 May 2020
  • CC1: Comment on EGU2020-22673, Paul Selvadurai, 03 May 2020

    Very nice poster. Thank you for the information.

    Does your model take into account the possible shear stress heterogeneities imposed by the known foreshock sequence believed to have ruptured portions of the faults that participated in the mainshock? I think this was shown by Dodge et al. (1995, 1995 , ).  Would localized dearths in shear stress affect your results? 

    Perhaps I missed a clear point. Apologies if so.

    • AC1: Reply to CC1, Nico Schliwa, 04 May 2020

      Thanks for the feedback!

      The magnitudes of the principal stresses vary only with depth, but the orientation of the maximum compressional stress axis changes smoothly. Also, the complex fault geometry alters the fault orientation with respect to the stress field, which has an impact on initial shear stresses and therefore produces heterogeneities.

      The simulations are from Wollherr et al. 2019 (“Landers 1992 “Reloaded”: Integrative Dynamic Earthquake Rupture Modeling”). There you find a detailed description of the model.

      • CC2: Reply to AC1, Paul Selvadurai, 04 May 2020

        Thanks Nico,

        Thanks for the reference, their work looks extensive and very infromative.

        I did look at the inital conditions and saw the ratio R (stress drop/strength drop). I wonder about largest FS that Dodge et al. (1995) recorded Mw 4.4 that occured within 7 hours of the mainshock. They found this to have a local stress drop of 2 MPa over a patch approxiamtely 1 km in diameter. While this is small compared to the overall mainshock rupture (~60 km), how do you think this sharp local contrast in R would change the simulations? Honestly just curious since this region of shear stress deficit is ~10 times the width of the cohesive zone in the prefered model. 

        • AC2: Reply to CC2, Nico Schliwa, 06 May 2020

          Hi Paul,

          I had a look into Dodge et al. 1995 and if I understand it right, they just analyzed the influence of static stress changes on other foreshocks. Also, the Mw 4.4 foreshock initiated just about 3s before the main event (Abercrombie and Mori, 1994), so its impact was probably more of dynamic than static nature.

          The question, if a localized deficit in shear stress due to foreshock activity in general could have a considerable effect on dynamic rupture simulations, I unfortunately can’t answer you at the moment. But if I get any information about this, I will let you know.