A parameterization study for elastic VTI full-waveform inversion of hydrophone components

A parameterization study for elastic VTI full-waveform inversion of hydrophone components

Antoine Guitton and Tariq Alkhalifah, "A parameterization study for elastic VTI full-waveform inversion of hydrophone components: Synthetic and North Sea field data examples ", Geophysics 82 (2017): R299-R308. DOI: 10.1190/geo2017-0073.1
Antoine Guitton, Tariq Alkhalifah
full-waveform inversion, anisotropy, VTI, North Sea, least squares
2017
​Choosing the right parameterization to describe a transversely isotropic medium with a vertical symmetry axis (VTI) allows us to match the scattering potential of these parameters to the available data in a way that avoids a potential tradeoff and focuses on the parameters to which the data are sensitive. For 2D elastic full-waveform inversion in VTI media of pressure components and for data with a reasonable range of offsets (as with those found in conventional streamer data acquisition systems), assuming that we have a kinematically accurate normal moveout velocity ( vNMO ) and anellipticity parameter  η  (or horizontal velocity  vh ) obtained from tomographic methods, a parameterization in terms of horizontal velocity  vh ,  η , and  ε  is preferred to the more conventional parameterization in terms of  vv ,  δ , and  ε . In the  vh ,  η , and  ε parameterization and for reasonable scattering angles (< 60° ),  ε  acts as a “garbage collector” and absorbs most of the amplitude discrepancies between the modeled and observed data, more so when density  ρ  and S-wave velocity  VS  are not inverted for (a standard practice with streamer data). On the contrary, in the  vv ,  δ , and  ε parameterization,  ε  is mostly sensitive to large scattering angles, leaving  vv  exposed to strong leakages from  ρ  mainly. These assertions will be demonstrated on the synthetic Marmousi II as well as a North Sea ocean bottom cable data set, in which inverting for the horizontal velocity rather than the vertical velocity yields more accurate models and migrated images.
(print): 0016-8033 (online): 1942-2156