We constructed an apparent geological model with resistivity data from surface resistivity surveys. We developed a data fusion approach by integrating dense electrical resistivity measurements collected with Schlumberger arrays and wellbore logs. This approach includes an optimization algorithm and a geostatistic interpolation method. We first generated an apparent formation factor model from the surface resistivity measurements and groundwater resistivity records with an inverse distance method. We then converted the model into a geology model with the optimized judgment criteria from the algorithms relating the apparent formation factors to the borehole geology. We also employed a non-parametric bootstrap method to analyze the uncertainty of the predicted sediment types, and the predictive uncertainties of clay, gravel, and sand were less than 5%. Overall, our model is capable of capturing the spatial features of the sediment types. More importantly, this approach can be arranged in a self-updated sequence to enable adjustments to the model to accommodate newly collected core records or geophysical data. This approach yields a more detailed apparent geological model for use in future groundwater simulations, which is of benefit to multi-discipline studies.