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

Caique Rodrigues Marques, Vinicius Guedes dos Santos, Rafael Lunelli, Mauro Roisenberg () and Bruno Barbosa Rodrigues
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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

Energies, 2022, vol. 15, issue 20, 1-20

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.

Keywords: Deep Learning; deep neural networks; seismic impedance inversion; benchmark (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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