SHORT COURSES

Level: Introductory

Facilitators: Tom Meuzelaar & Sam Wright, Life Cycle Geo, Pim van Geffen, CSA Global and Juan Carlos Ordonez Calderon, Kinross

An introductory workshop designed for professionals working in all stages of the mine project life cycle.

The participants will be introduced to machine learning methods that can be applied to exploration, mine to mill optimization, and environmental planning. Practical demonstrations using Python and/or Orange will be performed using mostly geochemical datasets.

Morning session: Sam Wright

Data management and machine learning concepts will be introduced at a high-level and framed within the context of the mine project life cycle. The workshop will highlight techniques that help improve accuracy and efficiency of algorithms and/or workflows including best practices. Practical exercises will use either Python and open-source software such as Orange.

Topics covered:

• Introduction to Statistics – emphasis on compositional datasets

• Introduction to Machine learning – Supervised and Unsupervised

• Principal Component Analysis, with emphasis on biplot interpretation

• Decision Trees

• Algorithms - Clustering/Regression/Classification

• Model evaluations – Feature engineering, model metrics

Afternoon session: Tom Meuzelaar, Pim van Geffen, Juan Carlos Ordonez Calderon

Practical application of machine learning across the mine project life cycle will be featured. Case studies will emphasize methodology, pitfalls to consider, challenges and successes of employing advanced methods.

The session will end with a panel Q&A with all the presenters of the short course.

Short Course Objectives:

To provide participants with hands on examples of employing machine learning techniques across a mine project life cycle.

Target Audience:

Mining professionals working in all stages of the mine project life cycle and students.

About the instructors:

Tom founded Life Cycle Geo (LCG) in 2018. LCG specializes in providing innovative geoscience solutions to the mining sector across the project life cycle. LCG currently employs advanced statistical workflows to help its mining clients optimize site-wide water/rock management. Tom specializes in applied geology, geochemistry and data science and has more than twenty years of industry experience. He has supported the mining industry in solving numerous challenging water/rock related problems through all project life cycle stages, including complex mine materials characterization and predictive water quality projects with frequent extension into mine planning and adaptive management.

Sam joined Life Cycle Geo LLC in May 2023. He earned his M.Sc. in geology from Northern Arizona University in 2019, with a focus in metamorphic petrology, and his M.Sc. in Data Science from the University of North Carolina at Charlotte in December 2023. Samuel is deeply committed to bridging the gap between natural science and data analytics, employing advanced statistical techniques and applied machine learning to derive actionable insights from extensive geochemical datasets. He is enthusiastic about the applications of data science in areas such as sensor technology and the Internet of Things (IoT), autonomous decision-making through machine learning, and the development of software and visualization tools to support informed decisions in the natural resources sector. In his free time, he enjoys outdoor adventures with his partner and their pups.

Pim is an Associate Partner with ERM in Vancouver, Canada. He is the technical lead in geoscience and orebody knowledge, with 20 years of experience in exploration geochemistry and geometallurgy. He completed his MSc at Utrecht University in the Netherlands and his PhD at Queen’s University in Ontario. His work focuses on integrating geoscience throughout the mining value chain, bringing expertise in data analysis for material characterization and workflow optimization. Pim is a Fellow of the AAG and the SEG and volunteers on the Technical Advisory Committee of Geoscience BC.

Juan Carlos is Kinross Director of Geoscience. He is an economic geologist and geochemist with over 15 years of experience in exploration and mining operations in the base and precious metals space. His expertise includes field geology and geochemistry of mineral deposits and implementation of geostatistical and machine-learning applications to develop orebody models for optimization of mining operations, improve resource estimation, and predictive mineral exploration models for Greenfields, Brownfields, and Mine exploration. His field and mathematical geoscience applications have covered a range of mineral systems such as porphyry, epithermal, skarn, VMS, IOCG, sedimentary-hosted, orogenic, and mesothermal deposits in the Americas, Africa, Russia, and Europe.