In the integration step of conventional mineral prospectivity analysis approaches chronology of ore-forming subsystems is ignored leading to less reliable predictions. In this paper, we design and adapt recurrent neural network architectures, which have the ability of modelling sequence-related natural events, and a random forest algorithm to bring the temporal nature of ore-forming subsystems into prospectivity analysis procedure and to mitigate the aforementioned issue. A dataset of Pb-Zn mineralization in Semnan Province, Iran, is used to illustrate the procedure. The exploration targets in the prospectivity maps show excellent agreement with the deposit locations, demonstrating the importance of incorporating the chronology of ore-forming geological processes in targeting mineral deposits. This study links our understanding of the chronology of mineral system parameters to predictive modeling to support decision-making in mineral exploration targeting.
