Multi-dip Estimation in n Dimensions

Traonmilin, Yann; Lambaré, Gilles; Herrmann, P.; Deladerrière, N.; Garceran, K.

doi:10.3997/2214-4609.201400065

Cited by 5 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High definition tomography inverts densely picked RMO and dip data. RMO picking is performed in a continuous manner, thus gathering huge amounts of detailed RMO information obtained either tracking local coherency in multi-dimension (Traonmilin et al, 2009) or scanning parametric curves along common image gathers (CIG) (Siliqi et al, 2009). The multi-dimensional tracking approach is more accurate when the signal to noise ratio is sufficiently high, while the curve/surface picking methods can be preferred when signal to noise ratio is low.…”

Section: High Definition Tomographymentioning

confidence: 99%

High definition tomography brings velocities to light

Sioni

Guillaume

Lambaré

et al. 2012

SEG Technical Program Expanded Abstracts 2012

View full text Add to dashboard Cite

Velocity model building remains a crucial step in seismic depth imaging. A general drawback of conventional tomographic approaches is that the estimated velocity models do not conform enough to the structures. We present several applications of an innovative high resolution tomography that inverts densely picked dip and residual move-out data to reveal detailed structurally conformable velocities. The application to the synthetic 2D Marmousi II dataset offers the possibility to carefully assess the method. It demonstrates its ability to produce structurally conformable velocity models with a level of detail that promotes velocity attributes as an aid to geological interpretation. As such it is complementary to full waveform inversion for the interpretation of reflected waves. Finally we show an application to 3D marine dataset where obtained higher resolution velocity model results in improved focusing of migrated images and improved match to well velocities.

show abstract

Section: High Definition Tomographymentioning

confidence: 99%