An interpolation method taking into account inequality constraints: II. Practical approach

Common features of models for interpolation, consistent with a finite number of inequality constraints on the range of values of a variablez, are discussed. A method based on constrained quadratic minimization yielding kriging estimates when no constraints exist, is presented. A computationally efficient formulation of quadratic minimization is obtained by using results on duality in quadratic programming. Relevant properties of the optimal interpolator are derived in a simple, self-contained way. The method is applied to mapping of horizon depth and estimation of thickness of an oil-bearing formation.