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| Titel |
Massive joint inversion of seismological and gravity data on multi-core processors |
| VerfasserIn |
Rosaria Tondi, Andrea Morelli, Carlo Cavazzoni, Peter Danecek |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250034629
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| Zusammenfassung |
| In order to obtain accurate and reliable estimation of the major lithological properties of the
rocks within a studied volume, geophysics uses the joint information provided by different
geophysical data sets (gravimetric, magnetic, seismic, etc.). The representation by probability
density functions (pdfs) of the different types of information entering the problem may
provide the mathematical framework to formulate their combination. The resulting joint
posterior pdf is composed of two factors: the joint likelihood function, which is the product of
independent likelihood functions associated with each geophysical data set, and the
joint prior pdf (JPD). The Maximum Likelihood Estimator of the JPD leads to the
solution of the problem. Nevertheless, one key problem appear to limit the use of
this solver to an extensive range of real applications: information coming from
potential fields which implies the presence of dense matrices into the resolving
estimator. It is well known that dense matrix systems challenge rapidly both the
algorithms and the computing platforms and are not suited to high resolution 3D
geophysical analysis. In this paper we show how we parallelize our code and obtain
fast and reliable solution of the JPD in presence of large data-sets and information
coming from potential fields (e.g. the gravity field). Analysis of the correctness of
results and performance on different parallel environments show the portability
and the efficiency of the code. The code is applied to a real experiment, where we
succeed in recovering a 3D density distribution within the crust and upper mantle,
beneath the European continent, constrained by both seismological and gravity
data. |
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