Geological modeling using a recursive convolutional neural networks approach

Resource models are constrained by the extent of geological units that often depend on the lithology, alteration and mineralization. A three dimensional model of these geological units must be built from scarce information coming from drillholes and limited understanding about the geological setting in which the ore deposit is places. In this work, we present a new technique for multiple-point geostatistical simulation based on a recursive convolutional neural network approach (RCNN). The method requires conditioning data and a training image that depicts the type of geological structures expected to be found in the deposit. This training image is used to learn the patterns of categories found and these are imposed in the final simulated model conditioned by the categories found during logging at the actual drill-hole samples. A lithological modeling process is carried out in a copper deposit in Chile to demonstrate the method. Comparison with current techniques and spatial metrics are used to clarify concepts and RCNN properties. Also, strengths and weaknesses of the methodology are discussed by briefly reviewing the theoretical perspective and looking into some of its practical aspects.