Poster Presentation
Surface Geochemistry in Mineral Exploration: Modern Methods and Challenges
Large geochemistry datasets of surface samples (soil, stream sediment, till, etc) are now commonplace in many mining jurisdictions. These data are commonly private and confidential, but some governments offer publicly available and free to use data—both types often come with a variety of challenges. A common challenge is associated with simple data organization problems that are surprisingly difficult to overcome. Another significant challenge with historical datasets stems from different sampling, processing, and analytical methods that were used through time. The most significant challenge, however, is that the easy-to-see anomalies have most likely already been explored and claimed.
This poster presentation uses public surface geochemistry datasets to exemplify how a series of tools and workflows can be used to address these challenges. The approach to mineral exploration should be customized to the mineral system and geographical area of the project. A multi-element approach is used to identify chemical trends associated with the mineral system of interest and anomaly detection is focused on both ore elements and their associated pathfinders. Machine learning methods play an essential role in this multi-element approach to mineral exploration but must be customized for each exploration project.
Authors:
