Transforming exploration data through machine learning

Abstract

The application of machine learning to the process of collecting and analysing geological data in mineral exploration has the potential to transform the way explorers operate. The traditional process of plan – drill – observe – measure – analyse can be slow and lead to costly re-drill or re-sample programs. A common issue faced by exploration companies is the inconsistency in the way data has been collected and categorised. This complicates the task of data modelling when undertaking economic viability studies. Using machine learning, data can be cleansed and validated prior to starting the modelling process. There are several ways to streamline the process for the resource geologist, the first being feature identification through imagery. High quality DSLR cameras provide a tool for exploration companies to collect high quality imagery of core and chip trays. Machine learning algorithms can recognize features in the images such as colour, structures, veins, particle size and hand-written text. It is feasible for this data to be automatically collected and stored in a database. Drill hole databases record rock interval attributes like rock code, hardness, colour, grade, location, and geophysical measurements. These attributes could be used as a lithological signature to identify other instances of similar signatures within the database. This technique could be used for data consistency testing or to discover new information within the dataset. Finally, to illustrate the power of machine learning, a small research project is presented that successfully identified the regions of core tray imagery that contained drill core.