Amirkabir University of TechnologyAUT Journal of Mathematics and Computing2783-24495120240101Simulating mixture of sub-Gaussian spatial data19510410.22060/ajmc.2023.22015.1130ENSeyedeh SomayehMousaviDepartment of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), IranAdelMohammadpourDepartment of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Iran0000-0002-5079-7025Journal Article20221215Spatial datasets may contain extreme values and exhibit heavy tails. So, the Gaussianity assumption for the corresponding random field is not reasonable. A sub-Gaussian $\alpha$-stable (SG$\alpha$S) random field may be more suitable as a model for heavy-tailed spatial data. This paper focuses on geostatistical data and presents an algorithm for simulating SG$\alpha$S random fields.https://ajmc.aut.ac.ir/article_5104_9d05e74ba86ad20ba50061a70f9ba2e3.pdf