Exemplar-based synthesis of geology using kernel discrepancies and generative neural networks
arXiv:1809.07748
Abstract
We propose a framework for synthesis of geological images based on an exemplar image. We synthesize new realizations such that the discrepancy in the patch distribution between the realizations and the exemplar image is minimized. Such discrepancy is quantified using a kernel method for two-sample test called maximum mean discrepancy. To enable fast synthesis, we train a generative neural network in an offline phase to sample realizations efficiently during deployment, while also providing a parametrization of the synthesis process. We assess the framework on a classical binary image representing channelized subsurface reservoirs, finding that the method reproduces the visual patterns and spatial statistics (image histogram and two-point probability functions) of the exemplar image.
References in corpus (11)
- Adam: A Method for Stochastic Optimization
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- On the Convergence of Adam and Beyond
- Deep Image Prior
- Reconstruction of three-dimensional porous media using generative adversarial neural networks
- Generative Moment Matching Networks
- A Kernel Method for the Two-Sample Problem
- Demystifying Neural Style Transfer
- Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
- Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models