Mathematical Geology

, Volume 31, Issue 6, pp 651–684

Geostatistical Space–Time Models: A Review


  • Phaedon C. Kyriakidis
    • Department of Geological and Environmental SciencesStanford University
  • André G. Journel
    • Department of Petroleum EngineeringStanford University

DOI: 10.1023/A:1007528426688

Cite this article as:
Kyriakidis, P.C. & Journel, A.G. Mathematical Geology (1999) 31: 651. doi:10.1023/A:1007528426688


Geostatistical space–time models are used increasingly for addressing environmental problems, such as monitoring acid deposition or global warming, and forecasting precipitation or stream flow. Each discipline approaches the problem of joint space–time modeling from its own perspective, a fact leading to a significant amount of overlapping models and, possibly, confusion. This paper attempts an annotated survey of models proposed in the literature, stating contributions and pinpointing shortcomings. Stochastic models that extend spatial statistics (geostatistics) to include the additional time dimension are presented with a common notation to facilitate comparison. Two conceptual viewpoints are distinguished: (1) approaches involving a single spatiotemporal random function model, and (2) approaches involving vectors of space random functions or vectors of time series. Links between these two viewpoints are then revealed; advantages and shortcomings are highlighted. Inference from space–time data is revisited, and assessment of joint space–time uncertainty via stochastic imaging is suggested.

space–time modelsgeostatisticstime seriestrend modelsstochastic simulation

Copyright information

© International Association for Mathematical Geology 1999