© 2015 Elsevier B.V. Buffer zones in bo\th two-dimensional (2D) and three-dimensional (3D) spaces are commonly used in prospectivity mapping. The method completes a modelling that starts with a real example and progresses to the development of a virtual model. This includes the consideration of lithological or structural contacts at depth, which is a theoretical concept based on extrapolation of data collected in the field, rather than an empirical observation of the feature based on physical samples. This contribution documents an improved buffer analysis method for the study of 3D-space that is implicit (rapid), precise (smooth) and based on triangulated characteristics, which can be used to construct influence domains of geological models. As traditional 2D GIS-based mineral potential mapping is gradually becoming limited with time, mineral potential mapping in three dimensions (3D) is increasingly becoming an important tool in finding concealed economic mineralization. This contribution documents an improved methodology of buffer analysis for prospectivity mapping processing mineralized favourable models rather than describing an advance in the geometry of surface rendering of "geological complexity". Measures used in this buffer analysis include the: (1) voxelization of geological objects (i.e. assigning numerical values of features on a regular cube in 3D-space); (2) revision of the 3D Euclidean distance transform and the calculation of signed distance field; (3) extracting surfaces from the field; and (4) construction of a buffer-surface based on a "discrete smooth interpolation" (DSI) algorithm. Furthermore, this contribution constructs 3D models using a buffer analysis algorithm and prospectivity mapping introduced here, which is based on real data from the Jiama Cu-polymetallic deposit in Tibet and Daye Fe deposit in the Hubei Province, China. This contribution also presents a comparison between voxel and irregular triangle models, which illustrate that irregular triangle mesh buffer analysis (ITB) can improve modelling techniques for GIS-based 3D mineral potential mapping. The outcome is an increase in the accuracy of prospectivity mapping.