Recursive convolutional neural networks in a multiple-point statistics framework

This work proposes a new technique for Multiple-Point Statistics simulation based on a Recursive Convolutional Neural Network approach (RCNN). A study on the architecture of the network is done to ensure that the spatial structure of the phenomenon inferred from a training image and its associated uncertainty are properly captured. A sensitivity analysis over the main architecture parameters is performed on a two dimensional binary image. Statistical and spatial metrics are determined to quantify the impact of each parameter as well as the ability to capture the underlying phenomenon.