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Analysis of Deep Learning Neural Networks for Seismic Impedance Inversion: A Benchmark Study

Author

Listed:
  • Caique Rodrigues Marques

    (Computer Sciences and Statistics Department, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

  • Vinicius Guedes dos Santos

    (Computer Sciences and Statistics Department, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

  • Rafael Lunelli

    (Computer Sciences and Statistics Department, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

  • Mauro Roisenberg

    (Computer Sciences and Statistics Department, Federal University of Santa Catarina, Florianópolis 88040-900, Brazil)

  • Bruno Barbosa Rodrigues

    (Petrobras Research Center, Rio de Janeiro 20031-912, Brazil)

Abstract

Neural networks have been applied to seismic inversion problems since the 1990s. More recently, many publications have reported the use of Deep Learning (DL) neural networks capable of performing seismic inversion with promising results. However, when solving a seismic inversion problem with DL, each author uses, in addition to different DL models, different datasets and different metrics for performance evaluation, which makes it difficult to compare performances. Depending on the data used for training and the metrics used for evaluation, one model may be better or worse than another. Thus, it is quite challenging to choose the appropriate model to meet the requirements of a new problem. This work aims to review some of the proposed DL methodologies, propose appropriate performance evaluation metrics, compare the performances, and observe the advantages and disadvantages of each model implementation when applied to the chosen datasets. The publication of this benchmark environment will allow fair and uniform evaluations of newly proposed models and comparisons with currently available implementations.

Suggested Citation

  • Caique Rodrigues Marques & Vinicius Guedes dos Santos & Rafael Lunelli & Mauro Roisenberg & Bruno Barbosa Rodrigues, 2022. "Analysis of Deep Learning Neural Networks for Seismic Impedance Inversion: A Benchmark Study," Energies, MDPI, vol. 15(20), pages 1-20, October.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7452-:d:938389
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