and a fluctuation term Y(x) of mean zero with a m(x) called the drift, variogram y(h) called the underlying variogram. The drift function m(x) varies slowly and can be modeled, at least locally, as k m(x) = > by f° L (x) > 2=0 where the £" (+) stand for known basic functions--monomials in practice-and the b, are unknown coefficients. Under this model E[Z(v)} =D, b, ef £*(x)dx = Dorf) L Vv and E[z (v)] = d BIZ (x,)] = g 2 dy 2, yf (x,)> so that & & £1. Gp) yf DoylD = 2@)1 BIz*(v) g i If we want the bias to be zero whatever the true unknown by (universal unbiasedness), we have to impose the conditions ya, (x,i ) = £*(v), ~ i 2 = 0,...,k 1 Minimizing E[ >» A, 20x) - Z(v) ]2 subject to the unbiasedness conditions i leads to the Universal Kriging system with k + 1 lagrange parameters Up? 2, yx, - x) +L upto) = (x, , v), J Q 2 DAf (x,) = £°), 2 = 0,...,k i=1,...,N UNIVERSAL KRIGING SYSTEM (9) L Naturally, Equation 8 is a special case of Equation 9, provided £9 (x) 21. At its minimum, the MSE (kriging variance) is oe = EZ(v) ~ 2(v)]? = Ey Wy 9 + Df) and provides a measure of the error of estimation. The inference of the variogram y(h) in the presence of a drift poses a serious problem. Indeed, the "raw' variogram 378