Constraining Frictional Properties on Fault by Dynamic Rupture Simulations and Near-Field Observations

Author(s):  
Huihui Weng ◽  
Hongfeng Yang
2011 ◽  
Vol 110 (7) ◽  
pp. 074318 ◽  
Author(s):  
Jean Dahdah ◽  
Maria Pilar-Bernal ◽  
Nadège Courjal ◽  
Gwenn Ulliac ◽  
Fadi Baida

2020 ◽  
Author(s):  
Nico Schliwa ◽  
Alice-Agnes Gabriel

<p>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.</p><p>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. </p><p>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.</p><p>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.</p><p><em>References:</em></p><p>Hanks, T.C., 1980. The corner frequency shift, earthquake source models and Q.</p><p>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.</p><p>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.</p><p>Zhan, Z., 2020. Distributed Acoustic Sensing Turns Fiber‐Optic Cables into Sensitive Seismic Antennas. Seismological Research Letters, 91(1), pp.1-15.</p>


Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 968-977 ◽  
Author(s):  
Andrey V. Lebedev ◽  
Igor A. Beresnev

A model of nonlinearity of the contact between the vibrator baseplate and the ground is proposed to describe the distortion of vibroseis signals in the near‐field. A thin layer between the baseplate and the soil exhibits a strong nonlinear response because of the difference in its rigidity between the compression and tension phases. The model allows for a quantitative description of the transmission of seismic energy into the ground, including the observed harmonic distortion. However, the contact nonlinearity does not lead to the dependence of wave traveltimes on the amplitude of the force applied to the ground. This fact can be used in field observations to localize the source of the observed harmonic distortion.


1985 ◽  
Vol 118 (3-4) ◽  
pp. 301-310 ◽  
Author(s):  
Yasuhiro Umeda

2019 ◽  
Vol 109 (6) ◽  
pp. 2168-2186 ◽  
Author(s):  
Paul Peshette ◽  
Julian Lozos ◽  
Doug Yule ◽  
Eileen Evans

Abstract Investigations of historic surface‐rupturing thrust earthquakes suggest that rupture can jump from one fault to another up to 8 km away. Additionally, there are observations of jumping rupture between thrust faults ∼50  km apart. In contrast, previous modeling studies of thrust faults find a maximum jumping rupture distance of merely 0.2 km. Here, we present a dynamic rupture modeling parameter study that attempts to reconcile these differences and determines geometric and stress conditions that promote jumping rupture. We use the 3D finite‐element method to model rupture on pairs of thrust faults with parallel surface traces and opposite dip orientations. We vary stress drop and fault strength ratio to determine conditions that produce jumping rupture at different dip angles and different minimum distance between faults. We find that geometry plays an essential role in determining whether or not rupture will jump to a neighboring thrust fault. Rupture is more likely to jump between faults dipping toward one another at steeper angles, and the behavior tapers down to no rupture jump in shallow dip cases. Our variations of stress parameters emphasize these toward‐orientation results. Rupture jump in faults dipping away from one another is complicated by variations of stress conditions, but the most prominent consistency is that for mid‐dip angle faults rupture rarely jumps. If initial stress conditions are such that they are already close to failure, the possibility of a long‐distance jump increases. Our models call attention to specific geometric and stress conditions where the dynamic rupture front is the most important to potential for jumping rupture. However, our models also highlight the importance of near‐field stress changes due to slip. According to our modeling, the potential for rupture to jump is strongly dependent on both dip angle and orientation of faults.


2016 ◽  
Vol 25 (9) ◽  
pp. 094222 ◽  
Author(s):  
Lei Wang ◽  
Shijun Ge ◽  
Zhaoxian Chen ◽  
Wei Hu ◽  
Yanqing Lu

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