Multiple-point Geostatistics: Stochastic Modeling with Training Images

By Gregoire Mariethoz, Jef Caers. 2014

448 citations recorded by the source corpus.

Subfields: Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/ca32e1d351df18bf44cb9122fc75cd30f57018b1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Philippe Renard
2. Niklas Linde
3. Tapan Mukerji
4. Jef Caers
5. Diederik Jacques
6. Martin J. Blunt
7. Olivier Dubrule
8. Lukas Mosser

Citations in the corpus

• Reconstruction of three-dimensional porous media using generative adversarial neural networks (2017, methodology): “The computational complexity of these methods is highly dependent on individual algorithms as well as the size of the domains used to sample from the training images [22].”
• Training‐Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network (2017, background): “Capturing subsurface complexity and connectivity is of utmost importance for reliable groundwater modeling and uncertainty quantification (e.g., Mariethoz and Caers , 2014; Linde et al. , 2015).”
• Geological realism in hydrogeological and geophysical inverse modeling: A review (2015, methodology): “Stien and Kolbjornsen [157] show applications for MMM for facies modeling (see also Chapter 4 in [114]).”