The correct estimation of density is crucial for the economic evaluation of mineral deposits. However, the current panorama reveals notable disparities in Quality Assurance and Quality Control (QA/QC) practices between density and grade estimations. Despite ongoing initiatives to standardize procedures, many mining companies use subjective judgements when determining the quantity and spatial distribution of density samples. As a result, the datasets in the mining industry often have drillholes with fewer density samples than grade samples. Due to limited drillhole samples, considering geophysical information is important to improve the accuracy and precision of the estimates. In this context, some mining companies are investing in acquiring found gravimetric surveys. However, this information obtained from gravimetry is usually neglected to obtain an estimated density model (by geophysical gravimetric inversion). For that reason, in this paper, we investigate how to consider both drillhole samples and gravimetry survey to improve the accuracy and precision of density models. We propose to use simple kriging with a local varying mean (SKLM), being the local mean a density derived from the gravimetry survey. Specifically, this density is obtained from the geophysical data from geophysical inversion. We applied the methodologies in a case study with synthetic data. The results showed a considerable correlation between validation data and estimated model, also the visual inspection exposed the general geological trends even when it was not controlled by geological domains. Finally, in conclusion, it was possible to demonstrate how to link both information and how the geophysical gravimetric inversion could be used in geostatistical processes being an ever better alternative if used for short-term mine planning.