Geometric anisotropy is commonly assumed in the investigation of the spatial variations of geophysical parameters. However, this assumption is not always satisfied in practice. We propose a novel method to determine the anisotropy of geophysical parameters. In the proposed method, the variograms are first normalized in all directions. Then, the normalized samples are fitted by the unit range variation increment (URVI) function to estimate the intensities of the variograms in each direction, from which the anisotropy can be finally determined. The performance of the proposed method is validated using InSAR atmospheric delay measurements over the Shanghai region. The results show that the deviation of the method is 6.4%, and that of the geometric anisotropy-based method is 21.2%. In addition, the computational efficiency of the new method is much higher. Subsequently, the URVI- and the geometric anisotropy-based methods are cross-validated in the cross-validation experiments by using Kriging interpolation. The results demonstrate that the structure functions generated with the proposed method are more accurate and can better refl ect the spatial characteristics of the randomfi eld. Therefore, the proposed method, which is more accurate and effi cient to determine the anisotropy than the conventional geometry anisotropy-based method, provides a better foundation to estimate the geophysical parameters of interest.