Polynomial surrogates for Bayesian traveltime tomography
نویسندگان
چکیده
This paper tackles the issue of computational load encountered in seismic imaging by Bayesian traveltime inversion. In inference, exploration posterior distribution velocity model parameters requires extensive sampling. The cost this sampling step can be prohibitive when first arrival prediction involves resolution an expensive number forward models based on eikonal equation. We propose to rely polynomial chaos surrogates traveltimes between sources and receivers alleviate burden solving equation during stage. offline stage, approach builds a functional approximation from set solutions corresponding few values selected their prior range. These then substitute eikonal-based predictions evaluation, enabling very efficient posterior, for instance, Markov Chain Monte Carlo algorithm. demonstrate potential using numerical experiments inference two-dimensional domains with layered different acquisition geometries (microseismic refraction contexts). results show that, our experiments, evaluations required construct accurate is low. Further, complete characterization possible, thanks generation large sample sets at low cost. Finally, we discuss extension current more realistic operational situations.
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ژورنال
عنوان ژورنال: Gem - International Journal on Geomathematics
سال: 2021
ISSN: ['1869-2680', '1869-2672']
DOI: https://doi.org/10.1007/s13137-021-00184-0