This paper illustrates a seismic tomographic imaging technique using stochastic a priori information about structural geological morphology. The method is based on a multiresolution representation, which allows incorporating into conventional Markov Random Field models probabilistic constraints between different scales. A MAP Bayesian iterative solution is proposed to perform inversion of largely ill conditioned problems in presence of a limited angular coverage and a limited number of ray-paths.
Multiresolution tomographic inversion from an incomplete data set / Iacovitti, Giovanni; A., Neri; S., Puledda. - 3169:(1997), pp. 260-271. (Intervento presentato al convegno Conference on Wavelet Applications in Signal and Image Processing V tenutosi a SAN DIEGO, CA nel JUL 30-AUG 01, 1997) [10.1117/12.279690].
Multiresolution tomographic inversion from an incomplete data set
IACOVITTI, Giovanni;
1997
Abstract
This paper illustrates a seismic tomographic imaging technique using stochastic a priori information about structural geological morphology. The method is based on a multiresolution representation, which allows incorporating into conventional Markov Random Field models probabilistic constraints between different scales. A MAP Bayesian iterative solution is proposed to perform inversion of largely ill conditioned problems in presence of a limited angular coverage and a limited number of ray-paths.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
