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Recursive convolutional neural networks in a multiple-point statistics framework
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DOI：10.1016/j.cageo.2020.104522.png)
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Abstract
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This work proposes a new technique for multiple-point statistics simulation based on a recursive convolutional neural network approach coined RCNN. The work focuses on methodology and implementation rather than performance to demonstrate the potential of deep learning techniques in geosciences. Two and three dimensional case studies are carried out. A sensitivity analysis is presented over the main RCNN structural parameters using a well-known training image of channel structures in two dimensions. The optimum parameters found are applied into image reconstruction problems using two other training images. A three dimensional case is shown using a synthetic lithological surface-based model. The quality of realizations is measured by statistical, spatial and accuracy metrics. The RCNN method is compared to standard MPS techniques and an improving framework is proposed by using the RCNN E-type as secondary information. Strengths and weaknesses of the methodology are discussed by reviewing the theoretical and practical aspects.
Keywords:
Geostatistics
Multiple-point statistics
Training image
Categorical variable
Deep learning
Convolutional neural networks
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