Comparison of recursive neural network and Markov chain models in facies inversion

DOI10.1007/S11004-020-09914-WzbMath1465.86008OpenAlexW3120809581MaRDI QIDQ2040689

Dario Grana, Erick Talarico, Wilson Leão

Publication date: 14 July 2021

Published in: Mathematical Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11004-020-09914-w

zbMATH Keywords

neural networkMarkov chainmachine learningfacies classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Markov processes: estimation; hidden Markov models (62M05) Inverse problems in geophysics (86A22) Geostatistics (86A32) Neural nets and related approaches to inference from stochastic processes (62M45)

Uses Software

• ElemStatLearn
• PRMLT

Cites Work

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• Petro-elastic log-facies classification using the expectation-maximization algorithm and hidden Markov models
• Representing spatial uncertainty using distances and kernels
• A Markov chain model for subsurface characterization: Theory and applications
• Modeling conditional distributions of facies from seismic using neural nets
• Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network
• Approximate posterior distributions for convolutional two-level hidden Markov models
• Bayesian Gaussian mixture linear inversion for geophysical inverse problems
• Estimation of geological attributes from a well log: an application of hidden Markov chains

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