Simons Mark

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  • Article
    Locations of selected small earthquakes in the Zagros mountains
    (American Geophysical Union, 2005-03-01) Lohman, Rowena B. ; Simons, Mark
    The Zagros mountains of southern Iran are marked by a zone of high seismicity and accommodate a significant portion of the convergence between Arabia and Eurasia. Due to the lack of dense local seismic or geodetic networks, the inferred kinematics of the collision in Iran is mainly based on catalogs of teleseismically determined earthquake locations. We surveyed all M w > 4.5 earthquakes in the Harvard Centroid Moment Tensor (HCMT) and International Seismological Centre (ISC) catalogs that occurred in the Zagros mountains during the period 1992–2002 and that were spanned by Interferometric Synthetic Aperture Radar (InSAR) images from the ERS 1 and 2 satellites. We invert the observed deformation for the best fitting point source, single fault plane, and distributed fault slip for four earthquakes and one unexplained deformation event. We find that we can precisely locate earthquakes that are too small to be well-located by either the HCMT or ISC catalogs, allowing us to tie specific earthquakes to active geologic structures.
  • Article
    Some thoughts on the use of InSAR data to constrain models of surface deformation : noise structure and data downsampling
    (American Geophysical Union, 2005-01-25) Lohman, Rowena B. ; Simons, Mark
    Repeat-pass Interferometric Synthetic Aperture Radar (InSAR) provides spatially dense maps of surface deformation with potentially tens of millions of data points. Here we estimate the actual covariance structure of noise in InSAR data. We compare the results for several independent interferograms with a large ensemble of GPS observations of tropospheric delay and discuss how the common approaches used during processing of InSAR data affects the inferred covariance structure. Motivated by computational concerns associated with numerical modeling of deformation sources, we then combine the data-covariance information with the inherent resolution of an assumed source model to develop an efficient algorithm for spatially variable data resampling (or averaging). We illustrate these technical developments with two earthquake scenarios at different ends of the earthquake magnitude spectrum. For the larger events, our goal is to invert for the coseismic fault slip distribution. For smaller events, we infer the hypocenter location and moment. We compare the results of inversions using several different resampling algorithms, and we assess the importance of using the full noise covariance matrix.