8,182 research outputs found
Eigenvector Model Descriptors for Solving an Inverse Problem of Helmholtz Equation: Extended Materials
We study the seismic inverse problem for the recovery of subsurface
properties in acoustic media. In order to reduce the ill-posedness of the
problem, the heterogeneous wave speed parameter to be recovered is represented
using a limited number of coefficients associated with a basis of eigenvectors
of a diffusion equation, following the regularization by discretization
approach. We compare several choices for the diffusion coefficient in the
partial differential equations, which are extracted from the field of image
processing. We first investigate their efficiency for image decomposition
(accuracy of the representation with respect to the number of variables and
denoising). Next, we implement the method in the quantitative reconstruction
procedure for seismic imaging, following the Full Waveform Inversion method,
where the difficulty resides in that the basis is defined from an initial model
where none of the actual structures is known. In particular, we demonstrate
that the method is efficient for the challenging reconstruction of media with
salt-domes. We employ the method in two and three-dimensional experiments and
show that the eigenvector representation compensates for the lack of low
frequency information, it eventually serves us to extract guidelines for the
implementation of the method.Comment: 45 pages, 37 figure
Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications
Many scientific problems require multiple distinct computational tasks to be
executed in order to achieve a desired solution. We introduce the Ensemble
Toolkit (EnTK) to address the challenges of scale, diversity and reliability
they pose. We describe the design and implementation of EnTK, characterize its
performance and integrate it with two distinct exemplar use cases: seismic
inversion and adaptive analog ensembles. We perform nine experiments,
characterizing EnTK overheads, strong and weak scalability, and the performance
of two use case implementations, at scale and on production infrastructures. We
show how EnTK meets the following general requirements: (i) implementing
dedicated abstractions to support the description and execution of ensemble
applications; (ii) support for execution on heterogeneous computing
infrastructures; (iii) efficient scalability up to O(10^4) tasks; and (iv)
fault tolerance. We discuss novel computational capabilities that EnTK enables
and the scientific advantages arising thereof. We propose EnTK as an important
addition to the suite of tools in support of production scientific computing
Neutrino tomography - Learning about the Earth's interior using the propagation of neutrinos
Because the propagation of neutrinos is affected by the presence of Earth
matter, it opens new possibilities to probe the Earth's interior. Different
approaches range from techniques based upon the interaction of high energy
(above TeV) neutrinos with Earth matter, to methods using the MSW effect on the
neutrino oscillations of low energy (MeV to GeV) neutrinos. In principle,
neutrinos from many different sources (sun, atmosphere, supernovae, beams etc.)
can be used. In this talk, we summarize and compare different approaches with
an emphasis on more recent developments. In addition, we point out other
geophysical aspects relevant for neutrino oscillations.Comment: 22 pages, 9 figures. Proceedings of ``Neutrino sciences 2005:
Neutrino geophysics'', December 14-16, 2005, Honolulu, USA. Minor changes,
some references added. Final version to appear in Earth, Moon, and Planet
Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion
We introduce a technique to compute exact anelastic sensitivity kernels in
the time domain using parsimonious disk storage. The method is based on a
reordering of the time loop of time-domain forward/adjoint wave propagation
solvers combined with the use of a memory buffer. It avoids instabilities that
occur when time-reversing dissipative wave propagation simulations. The total
number of required time steps is unchanged compared to usual acoustic or
elastic approaches. The cost is reduced by a factor of 4/3 compared to the case
in which anelasticity is partially accounted for by accommodating the effects
of physical dispersion. We validate our technique by performing a test in which
we compare the sensitivity kernel to the exact kernel obtained by
saving the entire forward calculation. This benchmark confirms that our
approach is also exact. We illustrate the importance of including full
attenuation in the calculation of sensitivity kernels by showing significant
differences with physical-dispersion-only kernels
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