GOCAD Mining Suite – The construction of plausible geological models
Available on our YouTube channel
In this lecture, Jean-Philippe Paiement shows the use of structural restoration tools in GMS as an integrated modelling tool for structurally controlled deposits. SKUA-GOCAD enables, through the use of the stratigraphic modeller, restoration of faulted and folded surfaces in 2D and 3D. The restoration tool (Kine 3D-1) allows the user to unfold and unfault complex surfaces to their original geometry. It also estimates key structural properties such as the strain and the dilatational component of the given strain. Since dilatational sites in compressive regimes are key in focusing hydrothermal fluids, it is a useful tool in exploring for structurally control mineral deposits, such as vein hosted orogenic gold.
Notes about the software:
SKUA-GOCAD implicit modelling is a powerful solution for 3D interpolation, analysis, modelling, data management, and interpretation. It allows you to faithfully represent uncertainty in your modelled structure and parameters, as well as noise in your data. It is perfect for the modelling of complex fault networks and stratigraphic horizons (with or without drillhole data). Implicit models allow you to look at or classify lithologies, interactively select and interpret the correct data, understand the various relationships, and assign and iteratively reassign “properties” within the interactive modelling environment.
Come to this session with your questions. You’ll be able to submit them directly to Jean-Philippe during our live Q&A session.
Jean-Philippe is the Director of Global Consulting. He is pursuing new applications of machine learning to overcome geological and geophysical challenges by combining geological knowledge with both supervised learning and deep learning. He brings 15 years of mineral exploration experience including expertise in geostatistics applied to structural, geological, and geochemical modelling and interpretation. Jean-Philippe has developed multiple workflow and novel approaches to reduce interpretational risks of geological data. He has a wide range of experience in mineral resource estimation for precious metals, base metals, and industrial minerals across diverse geological environments around the world.
In 2016, Jean-Philippe has pioneered the application of Machine Learning to the mineral exploration industry in winning the Integra GoldRush challenge by application of machine learning to mineral deposit targeting.
Before joining Mira Geoscience, Jean-Philippe worked in the private equity market, at SGS Canada Inc., and Teck Red Dog Mine. He obtained an MSc in metallogeny and geochemistry from Laval University. Jean-Philippe is based in Quebec-City.
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