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Schlagwörter:
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Zusammenfassung:
Dynamic rupture simulations generate synthetic waveforms that account for nonlinear
source and path complexity. Here, we analyze millions of spatially dense waveforms from
3D dynamic rupture simulations in a novel way to illuminate the spectral fingerprints
of earthquake physics. We define a Brune-type equivalent near-field corner frequency
(f c ) to analyze the spatial variability of ground-motion spectra and unravel their link
to source complexity. We first investigate a simple 3D strike-slip setup, including an asper-
ity and a barrier, and illustrate basic relations between source properties and f c varia-
tions. Next, we analyze > 13,000,000 synthetic near-field strong-motion waveforms
generated in three high-resolution dynamic rupture simulations of real earthquakes,
the 2019 Mw 7.1 Ridgecrest mainshock, the Mw 6.4 Searles Valley foreshock, and the
1992 Mw 7.3 Landers earthquake. All scenarios consider 3D fault geometries, topography,
off-fault plasticity, viscoelastic attenuation, and 3D velocity structure and resolve
frequencies up to 1–2 Hz. Our analysis reveals pronounced and localized patterns of
elevated f c , specifically in the vertical components. We validate such f c variability with
observed near-fault spectra. Using isochrone analysis, we identify the complex dynamic
mechanisms that explain rays of elevated f c and cause unexpectedly impulsive, localized,
vertical ground motions. Although the high vertical frequencies are also associated with
path effects, rupture directivity, and coalescence of multiple rupture fronts, we show that
they are dominantly caused by rake-rotated surface-breaking rupture fronts that decel-
erate due to fault heterogeneities or geometric complexity. Our findings highlight the
potential of spatially dense ground-motion observations to further our understanding
of earthquake physics directly from near-field data. Observed near-field f c variability
may inform on directivity, surface rupture, and slip segmentation. Physics-based models
can identify “what to look for,” for example, in the potentially vast amount of near-field
large array or distributed acoustic sensing data.
