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Structural similarity regularization scheme for multiparameter seismic full waveform inversion
Author(s) -
Maokun Li,
Lin Liang,
Aria Abubakar,
Schlumberger,
P.M. van den Berg
Publication year - 2013
Publication title -
data archiving and networked services (dans)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1190/segam2013-0537.1
Subject(s) - computer science , inversion (geology) , waveform , regularization (linguistics) , algorithm , similarity (geometry) , geology , artificial intelligence , seismology , telecommunications , image (mathematics) , radar , tectonics
We introduce a new regularization scheme for multiparameter seismic full-waveform inversion (FWI). Using this scheme, we can constrain spatial variations of parameters which are having a weak sensitivity with the one that having a good sensitivity to the measurement, assuming that these parameters have similarities in their structures. In seismic FWI, we apply this scheme when inverting the P-wave velocity and mass density simultaneously. Results from numerical tests show that this scheme may significantly improve the reconstruction of the mass density. Since we obtain an improved mass-density distribution, the inverted P-wave velocity is also enhanced. Hence, we also obtain a better data fit. As numerical examples we show inversions of both vertical seismic profiling (VSP) and surface seismic measurements.IST/Imaging Science and TechnologyApplied Science

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