Critical angle of reflections and Poisson's ratio from spectral recomposition

Nelson Ricardo Coelho Flores Zuniga*, Deyan Draganov, Ranajit Ghose

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Using the critical angle information of a reflection event, it is possible to calculate several essential physical parameters that are key to reliable geological characterization of the subsurface. However, estimation of the critical angle usually requires several steps of seismic processing. For this reason, an approach which is capable of estimating the critical angle directly from the data is of interest. Once the critical angle is estimated, it is possible to estimate further the Poisson's ratio and the seismic velocities. In this work, we propose an approach which can perform this estimation, based on spectral recomposition of seismic data. We design an inversion scheme in order to reconstruct the seismic spectrum of wavelets of a reflection event, which subsequently allows us to estimate the critical angle of near-surface reflection events without performing prior velocity analysis. After finding the critical angle, we show next how to estimate the Poisson's ratio and the compressional- and shear-wave velocities of the medium above the reflector. The approach leads to quite accurate values for Poisson's ratio even for noisy data, in case the number of layers is not large.

Original languageEnglish
Article number105110
Number of pages14
JournalJournal of Applied Geophysics
Volume215
DOIs
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Critical angle
  • Frequency spectrum
  • Inversion
  • Poisson's ratio
  • Spectral recomposition

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