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

2018/09/20 by Shing Chan, Chan, Shing, Ahmed H. Elsheikh +1 · 1 citation
Computer Science · Earth and Planetary Sciences · Engineering · #Generative Adversarial Networks and Image Synthesis #Seismic Imaging and Inversion Techniques #Reservoir Engineering and Simulation Methods

paper · pdf · doi:10.48550/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.

Citations

• Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks

Cited by

• History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions

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