Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder

Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder

Seismic facies analysis (SFA) is a crucial step in the interpretation of subsurface structures, with the core challenge being the development of automatic approaches for the analysis of 4D prestack seismic data. The dominant isolated learning-based SFA schemes have gained considerable attention and...

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Bibliographic Details
Main Authors: Haowei Hua, Feng Qian, Gulan Zhang, Yuehua Yue
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep clustering
end-to-end learning
lognormal mixture-based variational autoencoder (LMVAE)
Seismic facies analysis (SFA)
Online Access:https://ieeexplore.ieee.org/document/10288056/

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