PAPIs Latam 2019

In order to identify new exploration targets to test potential zones of gold mineralization, geologists gather different sources of data such as drill hole observations and measurements to justify their decisions. Such activity is complex and requires expert tacit knowledge specific to each mining project, meaning that the location's underlying geology is unique. With the aim of aiding this decision making process, we present a machine learning methodology to predict the level of gold mineralization based on the surrounding geological information. In particular, we propose the use of Deep Learning models, 3D Convolution Neural Networks, to take advantage of the spatial nature of the data.  Through experiments with data from a real mine, we show that our proposed model overcomes the baseline model for all the metrics used, providing a much more accurate feature for the geologists in their investigations.