Constraint nip-tomographic inversion of strong sparse seismic data

• Published in: Journal of applied geophysics 2019-01, Vol.160, p.195-206
• Main Authors: Vieira, W.W.S., Leite, L.W.B., Pena, F.A.V.
• Format: Article
• Language: English
• Subjects: CRS-partial stack; Seismic NIP-tomography; Sparse data; Spectral analysis
• ISSN: 0926-9851; 1879-1859
• DOI: 10.1016/j.jappgeo.2018.09.017

This work is a result of specific numerical experiments motivated by real cases of processing strong sparse seismic data, as an application of techniques based on the common-reflection-surface (CRS) stack technology aiming at estimating a smooth velocity depth distribution. The paper is primarily limited to numerical tests with a depth velocity model that attends closely the paraxial theory validated by the seismic ray hypotheses. A complete modeling of a seismic survey was performed, and the common-shot sections were submitted to random muting of traces, to noise addition, and afterwards followed by reconstruction of the section by trace interpolation. The interpolation was controlled by the 2D spectral non-aliasing condition, where the t − x spectral amplitude content was limited to the two main Fourier quadrants f − k. It was admitted that most information was based on primary compressional (P) wave content; therefore, multiples and the P − S conversion were considered as noise. The trace interpolation used the stack attributes of the original gather (conventional stack) with sparse data to construct supergather sections (for the supergather stack). The velocity distribution in depth uses the principle of interpreting the inversion data as normal incidence point (NPI) information. The applied inversion algorithm is NIP-tomographic, classified as curve fitting, non-linear, multi-parametric, that uses the wave front kinematic and dynamic CRS attributes as data-driven constraints to estimate a consistent depth velocity distribution. As a general conclusion, we emphasized also interpolation, inclusive of sparse data, as a step for spectral analysis, consequently in filtering, stacking, and tomography to obtain a velocity distribution for further use in the estimation for velocity distribution, imaging, geological interpretation and sedimentary basin modeling. [Display omitted] •This work is a result of specific numerical experiments motivated on real cases of processing strong sparse seismic data.•The interpolation was controlled by the 2D spectral non-aliasing condition, where the t-x spectral amplitude content was limited to the two main Fourier quadrants f-k.•As a general conclusion, we emphasized also interpolation, inclusive of sparse data, as a step for spectral analysis, consequently in filtering, stacking, and tomography to obtain a velocity distribution for further use in the estimation for velocity distribution, imaging, geological interpretation