559 research outputs found
Role of scattering in virtual source array imaging
We consider imaging in a scattering medium where the illumination goes
through this medium but there is also an auxiliary, passive receiver array that
is near the object to be imaged. Instead of imaging with the source-receiver
array on the far side of the object we image with the data of the passive array
on the near side of the object. The imaging is done with travel time migration
using the cross correlations of the passive array data. We showed in [J.
Garnier and G. Papanicolaou, Inverse Problems {28} (2012), 075002] that if (i)
the source array is infinite, (ii) the scattering medium is modeled by either
an isotropic random medium in the paraxial regime or a randomly layered medium,
and (iii) the medium between the auxiliary array and the object to be imaged is
homogeneous, then imaging with cross correlations completely eliminates the
effects of the random medium. It is as if we imaged with an active array,
instead of a passive one, near the object. The purpose of this paper is to
analyze the resolution of the image when both the source array and the passive
receiver array are finite. We show with a detailed analysis that for isotropic
random media in the paraxial regime, imaging not only is not affected by the
inhomogeneities but the resolution can in fact be enhanced. This is because the
random medium can increase the diversity of the illumination. We also show
analytically that this will not happen in a randomly layered medium, and there
may be some loss of resolution in this case.Comment: 22 pages, 4 figure
Array imaging of localized objects in homogeneous and heterogeneous media
We present a comprehensive study of the resolution and stability properties
of sparse promoting optimization theories applied to narrow band array imaging
of localized scatterers. We consider homogeneous and heterogeneous media, and
multiple and single scattering situations. When the media is homogeneous with
strong multiple scattering between scatterers, we give a non-iterative
formulation to find the locations and reflectivities of the scatterers from a
nonlinear inverse problem in two steps, using either single or multiple
illuminations. We further introduce an approach that uses the top singular
vectors of the response matrix as optimal illuminations, which improves the
robustness of sparse promoting optimization with respect to additive noise.
When multiple scattering is negligible, the optimization problem becomes linear
and can be reduced to a hybrid- method when optimal illuminations are
used. When the media is random, and the interaction with the unknown
inhomogeneities can be primarily modeled by wavefront distortions, we address
the statistical stability of these methods. We analyze the fluctuations of the
images obtained with the hybrid- method, and we show that it is stable
with respect to different realizations of the random medium provided the
imaging array is large enough. We compare the performance of the
hybrid- method in random media to the widely used Kirchhoff migration
and the multiple signal classification methods
Illumination strategies for intensity-only imaging
We propose a new strategy for narrow band, active array imaging of localized
scat- terers when only the intensities are recorded and measured at the array.
We consider a homogeneous medium so that wave propagation is fully coherent. We
show that imaging with intensity-only measurements can be carried out using the
time reversal operator of the imaging system, which can be obtained from
intensity measurements using an appropriate illumination strategy and the
polarization identity. Once the time reversal operator has been obtained, we
show that the images can be formed using its singular value decomposition
(SVD). We use two SVD-based methods to image the scatterers. The proposed
approach is simple and efficient. It does not need prior information about the
sought image, and guarantees exact recovery in the noise-free case.
Furthermore, it is robust with respect to additive noise. Detailed numerical
simulations illustrate the performance of the proposed imaging strategy when
only the intensities are captured
Statistical stability in time reversal
When a signal is emitted from a source, recorded by an array of transducers,
time reversed and re-emitted into the medium, it will refocus approximately on
the source location. We analyze the refocusing resolution in a high frequency,
remote sensing regime, and show that, because of multiple scattering, in an
inhomogeneous or random medium it can improve beyond the diffraction limit. We
also show that the back-propagated signal from a spatially localized
narrow-band source is self-averaging, or statistically stable, and relate this
to the self-averaging properties of functionals of the Wigner distribution in
phase space. Time reversal from spatially distributed sources is self-averaging
only for broad-band signals. The array of transducers operates in a
remote-sensing regime so we analyze time reversal with the parabolic or
paraxial wave equation
Large deviations for a mean field model of systemic risk
We consider a system of diffusion processes that interact through their
empirical mean and have a stabilizing force acting on each of them,
corresponding to a bistable potential. There are three parameters that
characterize the system: the strength of the intrinsic stabilization, the
strength of the external random perturbations, and the degree of cooperation or
interaction between them. The latter is the rate of mean reversion of each
component to the empirical mean of the system. We interpret this model in the
context of systemic risk and analyze in detail the effect of cooperation
between the components, that is, the rate of mean reversion. We show that in a
certain regime of parameters increasing cooperation tends to increase the
stability of the individual agents but it also increases the overall or
systemic risk. We use the theory of large deviations of diffusions interacting
through their mean field
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