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16.06.2024

Approximate Finite Rate of Innovation Based Seismic Reflectivity Estimation

verfasst von: P. Sudhakar Reddy, B. S. Raghavendra, A. V. Narasimhadhan

Erschienen in: Circuits, Systems, and Signal Processing | Ausgabe 10/2024

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Abstract

Reflectivity inversion is an important deconvolution problem in reflection seismology that helps to describe the subsurface structure. Generally, deconvolution techniques iteratively work on the seismic data for estimating reflectivity. Therefore, these techniques are computationally expensive and may be slow to converge. In this paper, a novel method for estimating reflectivity signals in seismic data using an approximate finite rate of innovation (FRI) framework, is proposed. The seismic data is modeled as a convolution between the Ricker wavelet and the FRI signal, a Dirac impulse train. Relaxing the accurate exponential reproduction limitation given by generalised Strang-Fix (GSF) conditions, we develop a suitable sampling kernel utilizing Ricker wavelet which allows us to estimate the reflectivity signal. The experimental results demonstrate that the proposed approximate FRI framework provides a better reflectivity estimation than the deconvolution technique for medium-to-high signal-to-noise ratio (SNR) regimes with nearly 18% of seismic data.

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Metadaten
Titel
Approximate Finite Rate of Innovation Based Seismic Reflectivity Estimation
verfasst von
P. Sudhakar Reddy
B. S. Raghavendra
A. V. Narasimhadhan
Publikationsdatum
16.06.2024
Verlag
Springer US
Erschienen in
Circuits, Systems, and Signal Processing / Ausgabe 10/2024
Print ISSN: 0278-081X
Elektronische ISSN: 1531-5878
DOI
https://doi.org/10.1007/s00034-024-02749-4