Integrated thin layer classification and reservoir characterization using sparse layer reflectivity inversion and radial basis function neural network: a case study-Reference-Cited by-同舟云学术

Integrated thin layer classification and reservoir characterization using sparse layer reflectivity inversion and radial basis function neural network: a case study

Published:2024-01-19 Issue:1 Volume:45 Page:
ISSN:0025-3235
Container-title:Marine Geophysical Research
language:en
Short-container-title:Mar Geophys Res

Author:

Singh Raghav,Srivastava Aditya,Kant Ravi,Maurya S. P.,Mahadasu P.,Verma Nitin,Hema G.,Kushwaha P. K.,Richa ,Singh K. H.,Singh Ajay P.,Srivastava M. K.,Sarkar Piyush

Funder

University Grants Commission

IoE BHU

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11001-023-09537-w.pdf

Reference41 articles.

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2. Broomhead DS, Lowe D (1988a) Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals and Radar Establishment Malvern (United Kingdom), ADA196234

3. Broomhead D, Lowe D (1988b) Multivariable functional interpolation and adaptive networks, complex systems, vol 2, pp 321-355

4. Chai X, Wang S, Yuan S, Zhao J, Sun L, Wei X (2014) Sparse reflectivity inversion for nonstationary seismic data. Geophysics 79(3):V93–V105

5. Chambers RL, Yarus JM (2002) Quantitative use of seismic attributes for reservoir characterization. CSEG Rec 27(6):14–25

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