Parallel Adaptive Mesh Coarsening for Seismic Tomography

Abstract : Seismic tomography enables to model the internal structure of the Earth. In order to improve the precision of existing models, a huge amount of acquired seismic data must be analyzed. The analysis of such massive data require a considerable computing power which can only be delivered by parallel computational equipments. Yet, parallel computation is not sufficient for the task: we also need algorithms to automatically concentrate the computations on the most relevant data parts. The objective of the paper is to present such an algorithm. From an initial regular mesh in which cells carry data with varying relevance, we present a method to aggregate elementary cells so as to homogenize the relevance of data. The result is an irregular mesh which has the ad- vantage over the initial mesh of having orders of magnitude less cells while preserving the geophysical meaning of data. We present both a sequential and a parallel algorithm to solve this problem under the hypotheses and constraints inherited from the geophysical context.
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Marc Grunberg, Stéphane Genaud, Catherine Mongenet. Parallel Adaptive Mesh Coarsening for Seismic Tomography. SBAC-PAD 2004, 16th Symposium on Computer Architecture and High Performance Computing, Oct 2004, Foz do Iguaçu, Brazil. ⟨10.1109/SBAC-PAD.2004.29⟩. ⟨inria-00504162⟩

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