Exemplar-based synthesis of geology using kernel discrepancies and generative neural networks

09/20/2018
by   Shing Chan, et al.
0

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.

READ FULL TEXT

page 6

page 7

page 8

page 10

page 11

page 12

page 18

page 19

research
12/02/2020

Two-sample test based on maximum variance discrepancy

In this article, we introduce a novel discrepancy called the maximum var...
research
12/11/2018

Bounding the Error From Reference Set Kernel Maximum Mean Discrepancy

In this paper, we bound the error induced by using a weighted skeletoniz...
research
05/14/2015

Training generative neural networks via Maximum Mean Discrepancy optimization

We consider training a deep neural network to generate samples from an u...
research
05/30/2023

Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification

This paper introduces a novel generator called Perturbation-Assisted Sam...
research
11/21/2021

Low-Discrepancy Points via Energetic Variational Inference

In this paper, we propose a deterministic variational inference approach...
research
09/05/2023

Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test

Maximum mean discrepancy (MMD) refers to a general class of nonparametri...
research
10/28/2019

PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems

Stochastic-sampling-based Generative Neural Networks, such as Restricted...

Please sign up or login with your details

Forgot password? Click here to reset