Kriging is a powerful geostatistical method used extensively for interpolating geological data and creating 3D geological models. It utilizes spatial correlation to predict values at unobserved location. Two widely-used types of Kriging are Ordinary Kriging (OK) and Universal Kriging (UK).

Ordinary Kriging is a robust and simple-to-implement algorithm that assumes a constant, but unknown, mean throughout the study area. It focuses on the spatial structure of the data, utilizing a variogram – a plot describing how data values change with distance and direction – to account for spatial autocorrelation. Ordinary Kriging is ideal for situations where the overall trend across the region is relatively flat or where local differences are more significant than the larger-scale trend.

Universal Kriging, on the other hand, takes into account both the spatial structure of the data and any spatial trend. It extends the capabilities of Ordinary Kriging by incorporating a deterministic component. Universal Kriging is particularly useful when the data exhibits a spatially varying trend.

Both Ordinary and Universal Kriging provide a means to quantitatively interpolate geological surfaces. Nonetheless, understanding their assumptions, strengths, and limitations is crucial for their effective use and for interpreting the resulting models.

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