Natural Resources Research

, Volume 28, Issue 1, pp 125–144 | Cite as

Multivariate Geostatistical Simulation on Block-Support in the Presence of Complex Multivariate Relationships: Iron Ore Deposit Case Study

  • Seyed Ali Hosseini
  • Omid AsghariEmail author
Original Paper


A reliable and realistic subsurface resource spatial modeling is a common and critical task for geosciences projects (mining, petroleum and environmental) because the models are then integrated with downstream processes to evaluate process performance. Coregionalized variables considered in resource modeling are often related through compositional constraints and complex dependence relationships. Satisfying these constraints and relationships in spatial modeling is a practical requirement to obtain accurate predictions of mineral resources in the subsurface. This paper presents a multistage method that addresses these issues and allows the multivariate simulation of cross-correlated variables with complex features directly on block-support, conditionally to the information of drill hole data at a quasi-point support. At a first stage, a chained transformation is used for removal multivariate complexities and decorrelation of the variables. Then, this chained transformation is adapted within the direct block-support sequential Gaussian simulation algorithm. The back-transformation of the chained transformation reintroduces complex features and correlations. A proof of the concept using a data set from the Gole-Gohar iron ore deposit in Iran demonstrates the performance of the proposed approach, the results of which are then compared against a common modeling approach.


Projection pursuit multivariate transform Flow anamorphosis Minimum/maximum autocorrelation factors Geostatistical simulation Multivariate modeling Change of support 



Special thanks apply to Raimon Tolosana-Delgado for guidance on running flow anamorphosis. Constructive comments from anonymous reviewers helped improve the manuscript.


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Copyright information

© International Association for Mathematical Geosciences 2018

Authors and Affiliations

  1. 1.Simulation and Data Processing Laboratory, School of Mining Engineering, University College of EngineeringUniversity of TehranTehranIran

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