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Mark Simons

Researcher at California Institute of Technology

Publications -  186
Citations -  13882

Mark Simons is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Interferometric synthetic aperture radar & Slip (materials science). The author has an hindex of 63, co-authored 176 publications receiving 11943 citations. Previous affiliations of Mark Simons include Massachusetts Institute of Technology.

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Practical implications of the geometrical sensitivity of elastic dislocation models for field geologic surveys

TL;DR: In this article, the sensitivity of the surface velocity field predicted by elastic dislocation models both early and late in the seismic cycle, to parameterizations of megathrust interface geometry, effective subducting plate thickness, and gradual transitions in apparent plate coupling was explored.
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A Stochastic View of the 2020 Elazığ M w 6.8 Earthquake (Turkey)

TL;DR: In this paper, the authors infer a stochastic model for the distribution of subsurface fault slip associated with the 2020 Elazig earthquake, which is characterized by two primary patches of fault slip where distribution appears to be controlled by geometrical complexities.
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Geodetic Imaging of Time-Dependent Three-Component Surface Deformation: Application to Tidal-Timescale Ice Flow of Rutford Ice Stream, West Antarctica

TL;DR: The algorithm extends an earlier single-component framework for time-series analysis to three spatial dimensions using combinations of multitemporal, multigeometry interferometic synthetic aperture radar (InSAR) and/or pixel offset (PO) maps and shows that the resulting three-component surface displacements resolve both secular motion and tidal variability.
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Probabilistic imaging of tsunamigenic seafloor deformation during the 2011 Tohoku-oki Earthquake

TL;DR: In this article, a probabilistic approach to image the spatiotemporal evolution of coseismic seafloor displacement from near-field tsunami observations was developed, where ensembles of nonlinear source models were sampled to focus on near-trench features, incorporating the uncertainty in modeling dispersive tsunami waves in addition to nominal observational errors.