57 citations · 61 across the 2 of their papers we have counts for
2 papers
cs.LG2019★ 4 cited
Generating Geological Facies Models with Fidelity to Diversity and Statistics of Training Images using Improved Generative Adversarial Networks
Lingchen Zhu, Tuanfeng Zhang
This paper presents a methodology and workflow that overcome the limitations of the conventional Generative Adversarial Networks (GANs) for geological facies modeling. It attempts…
stat.ML2018★ 57 cited
Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
Emilien Dupont, Tuanfeng Zhang, Peter Tilke +2
An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations. Typically, this is done using spati…