Senior Project Geologist
ALS Geoanalytics
Well-Shen has been involved in mining and mineral exploration for the past 10 years. He holds a PhD in Mineral Deposits from Laurentian University, Canada, and completed his undergraduate degree at the University of British Columbia, Canada. He has worked in a variety of exploration to feasibility-stage projects in Canada, the USA, and Botswana with experience on various base- and precious metal deposit types around the world in his current role at ALS Geoanalytics. He specializes in mineralogy, geochemistry, and machine learning.
Well-Shen is a geometallurgist and explorationist who routinely works at the intersection of various geoscience disciplines. Using the lens of mineralogy and its effect on geochemical, metallurgical, and physical data, Well-Shen has produced multiple predictive models for geologists, resource estimators, engineers, and metallurgists during his career.
Computer Vision and its Role in the Geoscientist’s Tool Kit: Case Study on a Copper Porphyry System in Arizona, USA.
Risk reduction in exploration and mining projects requires a good understanding of variability in the underlying geology. Mineralogy and texture are the largest drivers of variability in rocks, directly impacting the drivers of project economics. Multi-element assay data is routinely used as a proxy for mineralogy in trying to estimate rock variability; however, textural information is often poorly quantified during exploration and early mine development.
Computer vision is a technology that utilizes the information contained within pixels in routinely collected drillcore photographs through a process called image analysis. In this talk, we demonstrate the application of computer vision using the ALS LithoLens Artificial Intelligence platform to extract quantitative geotechnical, colour, and textural information from drillcore photographs at a copper porphyry project in Arizona, USA.
Through this case study, we demonstrate that combining image data with routine assays, magnetic susceptibility, and short-wave infrared (SWIR) datasets significantly improves the results of predictive lithology and regression models. The image data can also be used alone to build predictive models with rapid turnaround time relative to other routinely collected datasets. A near-miss predictive model for copper was able to account for 76% of rock variance using image data alone, with texture parameters ranked the highest in feature importance.
Computer vision is shown to be a valuable, complementary tool for geologists to improve data consistency, continuity, and remove subjectivity from the data collection process. This will in turn allow robust geometallurgical models to be built to reduce risk throughout the value chain.
