Justin Granek completed his MSc in Geophysics at the UBC Geophysical Inversion Facility with Doug Oldenburg in 2011 working on the incorporation of downhole information to constrain DC inversions. Upon graduation, Justin went to work for Computational Geosciences Inc as a project geophysicist, where he performed advanced 3D inversions of potential field, DCIP, and EM data for numerous mineral exploration clients.
In 2013 Justin went back to UBC-GIF to begin a PhD with Eldad Haber working on the development of machine learning algorithms for mineral prospectivity mapping, exploring algorithms such as support vector machines and neural networks. As an ideal case study for this problem, Justin has worked extensively on the QUEST project area in central British Columbia, including the available geological, geochemical and geophysical datasets.
Project: Advanced Geoscience Targeting via Focused Machine Learning
With the majority of the easy mineral resource targets having already been found, the mining industry is being forced to re-evaluate methodologies for conducting exploration programs. Future targets are likely to be deeper, with little or no surface expression, and may be concealed from conventional exploration techniques by overburden, permafrost or other geologically complex environments. In order to locate such targets, a holistic multidisciplinary approach is required to identify trends across multiple characteristic features, including geology, geophysics and geochemistry.
Quantifying and correlating georeferenced features from such a wide array of data types can be best handled using machine learning algorithms. These algorithms can be trained on a sub-sample of known mineral deposit locations such that they are able to identify the trends in the multi-parameter data set which are associated with mineralization. Once the relevant trend has been identified, a machine learning algorithm is able to predict new mineralization zones from the full data set. Many different machine learning algorithms exist, each with advantages and disadvantages. Tailoring an algorithm to this specific application requires an understanding of both the data, as well as the theory supporting machine learning. Such a marriage of expertise can lead to vast improvements in performance and a more reliable outcome in predicting future mineralization zones.