The estimation of missing observations is an important research field which has practical applications in many science and engineering disciplines. In analyzing the variability of a particular data set which can be spatially related, classical statistical methods make no use of this type of information; whereas geostatistics accomodates the spatial information of the data set in its regression ...

Stochastic simulation of facies or geologic units is important before the assignment of continuous rock properties. Sequential indicator simulation (SIS) remains a reasonable approach when there are no clear genetic shapes that could be put into object-based modeling. Constraining SIS to soft secondary data coming from geological interpretation or geophysical measurements is important. There ar...

A geostatistical analysis of soil salinity in an agricultural area in the San Joaquin Valley included measurements of electrical conductivity of soil paste extract (EC3 and water content of soil samples supplemented by surface measurements of apparent electrical conductivity (EM”). Prediction of soil salinity at unsampled points by cokriging log@C3 and EMa is worthwhile because EM” measurements...

Spatial variables in the geosciences often display different patterns of variability at different spatial scales. When these variables are cross-correlated, the magnitude of the cross-correlation may differ at the different scales of spatial variability. This scale-dependent spatial variability and cross-correlation can be investigated by geostatistical factor analysis, which consists of four s...

The two sources of information commonly available for modeling the top of a structure, depth data from wells and geophysical measurements from seismic surveys, are often Miicult to integrate. W bile, the well data provide t he most accurate measurements of depths there are rarely enough wells to permit an accurate appraisal from well data alone. On the other hand, the seismic data are generally...

Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geostatistics. While best prediction has been well understood univariate statistics, literature for case elusive so far. The new challenges provided by modern datasets, being typically multivariate, call a deeper study cokriging. In particular, we deal with problem misspecified cokriging wi...