Articles | Volume 16, issue 17
Model description paper
14 Sep 2023
Model description paper |  | 14 Sep 2023

AutoQS v1: automatic parametrization of QuickSampling based on training images analysis

Mathieu Gravey and Grégoire Mariethoz

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Cited articles

Abdollahifard, M. J., Baharvand, M., and Mariéthoz, G.: Efficient training image selection for multiple-point geostatistics via analysis of contours, Comput. Geosci., 128, 41–50,, 2019. 
Baninajar, E., Sharghi, Y., and Mariethoz, G.: MPS-APO: a rapid and automatic parameter optimizer for multiple-point geostatistics, Stoch. Environ. Res. Risk Assess., 33, 1969–1989,, 2019. 
Boisvert, J. B., Pyrcz, M. J., and Deutsch, C. V.: Multiple Point Metrics to Assess Categorical Variable Models, Nat. Resour. Res., 19, 165–175,, 2010. 
Dagasan, Y., Renard, P., Straubhaar, J., Erten, O., and Topal, E.: Automatic Parameter Tuning of Multiple-Point Statistical Simulations for Lateritic Bauxite Deposits, Minerals, 8, 220,, 2018. 
Gómez-Hernández, J. J. and Wen, X.-H.: To be or not to be multi-Gaussian? A reflection on stochastic hydrogeology, Adv. Water Resour., 21, 47–61,, 1998. 
Short summary
Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.