Abstract: In Kriging interpolation, the types of variogram model are very finite, which make the variogram very difficult to describe the spatial distributional characteristics of true data. In order ...

Prior to every geostatistical estimation or simulation study there is a need for delimiting the geologic domains of the deposit, which is traditionally done manually by a geomodeler in a laborious, ...

Experimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) is a new and advanced way of automating experimental variogram modelling. One part of ...

Abstract: Many existing variogram theoretical models which include nonlinear model, linear model and hole effect model had been existed. However, in the process of estimating the spatial distribution ...

Domaining plays a significant role when major statistical or geostatistical variations are observed across a site, which can influence the results of spatial modeling. In this paper, hard and fuzzy ...

Pyrcz, M.J., Jo. H., Kupenko, A., Liu, W., Gigliotti, A.E., Salomaki, T., and Santos, J., 2021, GeostatsPy Python Package: Open-source Spatial Data Analytics and ...

Package provides methodology for automated mapping i.e. spatial interpolation and/or prediction using Ensemble Machine Learning (extends functionality of the mlr package). Key functionality includes: ...