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Abstract

Full Waveform Inversion (FWI) is a geophysical tool that is designed to improve subsurface models by directly comparing, by means of a misfit function, synthetic traces obtained using an initial model with the real trace recorded experimentally. However, the high computational cost of the forward modeling combined with the huge size of the data, make FWI an extremely complex HPC problem. Nevertheless, its enhanced resolution compared to other seismic inversion methods is starting to make FWI an attractive subsurface imaging tool for both industry and academy. An FWI system workflow consists in processing the data (e.g filtering, windowing), launching gradi- ent computation simulations for each shot, merging the gradients, launching an optimization algorithm which involves executing modeling simulations and finally, updating the models with the merged gradients. On top of that, this is performed inside a minimization loop which is nested inside a frequency selection loop. This leads to complex interactions between nodes and therefore requires an extremely robust environment to be successful, in particular for large 3D inversions. Thus there is a challenge in terms of computational cost, but also of development cost, in order to obtain an FWI system which produces inverted models both reliably and efficiently.

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/content/papers/10.3997/2214-4609.20141937
2014-09-07
2020-04-04
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20141937
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