K-fold Cross-validation of Multiple-point Statistical Simulations

References

1. Davis, B.M. [1987] Uses and abuses of cross-validation in geostatistics. Mathematical geology, 19(3), 241–248.
   [Google Scholar]
2. Feng, W., Wu, S., Yin, Y., Zhang, J. and Zhang, K. [2017] A training image evaluation and selection method based on minimum data event distance for multiple-point geostatistics. Computers & Geosciences, 104, 35-53.
   [Google Scholar]
3. Kohavi, R. [1995] A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2, IJCAI’95. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1137–1143.
   [Google Scholar]
4. Mariethoz, G. and Caers, J. [2014] Multiple-point geostatistics: stochastic modeling with training images.John Wiley & Sons.
   [Google Scholar]
5. Mariethoz, G., Renard, P. and Straubhaar, J. [2010] The Direct Sampling method to perform multiple-point geostatistical simulations. Water Resources Research, 46(11), n/a-n/a. W11536.
   [Google Scholar]
6. Mariethoz, G., Straubhaar, J., Renard, P., Chugunova, T. and Biver, P. [2015] Constraining distance-based multipoint simulations to proportions and trends. Environmental Modelling & Software, 72, 184-197.
   [Google Scholar]
7. Meerschman, E., Pirot, G., Mariethoz, G., Straubhaar, J., Meirvenne, M.V. and Renard, P. [2013] A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm. Computers & Geosciences, 52, 307-324.
   [Google Scholar]
8. Pérez, C., Mariethoz, G. and Ortiz, J.M. [2014] Verifying the high-order consistency of training images with data for multiple-point geostatistics. Computers & Geosciences, 70, 190-205.
   [Google Scholar]
9. Straubhaar, J. [2019] DeeSse user’s guide. Tech. rep., The Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel: Neuchâtel, Switzerland.
   [Google Scholar]