Integration of heterogeneous field data and remote sensing information is a key and necessary step in modern geological exploration. This article proposes a method based on the creation of a regular spatial grid, which enables the efficient interpolation and integration of point, linear, and polygonal data represented in both vector and raster formats. The primary objective is to generate a structured and enriched dataset suitable for training predictive models, including neural networks. The proposed approach involves transforming geospatial data to ensure their accuracy and consistency within GIS environments. This method provides a reliable foundation for identifying prospective areas with high mineral potential and highlights the importance of rigorous data preparation in spatial modeling and analysis processes.

Keywords: reservoir exploration, data integration, interpolation, spatial grid, geochemistry, spatial modeling process, remote sensing, GIS