This paper presents new developments to the lateral parameter correlation method, a method that can be used to invoke lateral smoothness in model sections of one‐dimensional inversion models. The lateral parameter correlation method has three steps. First, all datasets are inverted individually. Next, a laterally smooth version of each model parameter is found by solving a simple constrained inversion problem by postulating identity between the uncorrelated and correlated parameters and solving the equations including a model covariance matrix that ensures lateral smoothness. As a final step, all sounding data are reinverted with the correlated model parameter values as a priori values to produce models that better fit the data. Because the method separates inversion and correlation, it is much faster than methods where the inversion and correlation are solved simultaneously. The new development to the lateral parameter correlation method presented in this paper is an option to perform a strictly horizontal correlation, thereby avoiding the model artefacts sometimes seen when correlating along layers, namely that formations tend to follow the topography. Furthermore, a solution to the intractable computation times arising with large datasets is formulated, employing a tessellation of the plane and an averaging scheme within the subareas that reduces the size of the numerical lateral parameter correlation inversion problem while maintaining correct correlation within a very large area. In a field example, correlation along layers and the new strictly horizontal correlation are compared, and it is demonstrated that the horizontal correlation removes the above mentioned artefacts that can appear when correlating along layers. The lateral parameter correlation method is very flexible and is capable of correlating models from inversion of different data types, including information from boreholes, and it lends itself easily to “embarrassingly parallel” computation.