Due to spatial auto-correlation and inter-correlation among geotechnical observed data

the spatial geotechnical characteristics distribution at unknown location has to be extrapolated or interpolated by some special methods. However

classical statistical methods could not rationally resolve the problems which include selection of sample points

and comparison of spatial estimating weights between bilateral data. The distance-weighted ordinary Kriging (OK)

universal Kriging (UK)

and co-Kriging (CK) prediction methods for scattered data are introduced at first

which are known as the Kriging family in global geostatistics. Moreover

multi-fractal theory combining with co-Kriging (MK) is presented to depict the local singularity which should be ignored by Kriging of sliding weighted average algorithm. The performance is compared in different typical geotechnical parameters: cohesion coefficient c

friction angle and compression modulus . This study is based on the main axis (525 m long

80 m wide) of Expo 2010 area of Shanghai

geotechnical test samples come from 42 boreholes. The performance of the different model fitness used in this study is MK

CK

OK and UK from the best to poor for parameters c and

and for parameter simulation

the sequence is MK

CK

UK and OK. The results prove that

in the most geotechnical occasions

auxiliary information would improve the prediction accuracy

and MK theory is useful tool to measure local singularity.