Taking a systematic approach should include all available inputs, used properly
Exploration will be transformed by a wholistic approach using traditional tools and new technology, rather than a silver bullet from the technology space.
Improving exploration success is about marrying the available tools with the right process to address a particular problem. That includes ensuring the right datasets are being used in the analysis process, and the right algorithms are being used to get the best results from an AI process but that’s not where the process starts.
Key tools like GIS and 3D modelling based on basic fieldwork, geological datasets and established geophysical techniques and processing have been fundamental for a long time and still very much remain relevant in exploration and defining mineral targets. But today, these datasets are now commonly feeding into a various statistical analysis and predictive methods, integrating with a wide range of datasets to target mineralisation. These datasets when integrated together can provide greater context to the mineral systems, as opposed to looking at the datasets in isolation to make exploration decisions. Applying machine learning or AI technology to these datasets is also further adding the suite of tools for predicting mineralisation, enhancing potential results and targets.
The goal that we are more regularly achieving when taking this approach is to build a systems framework capable of supporting predictive discovery.
While this may sound logical, we are seeing practical examples of exploration teams over-emphasising the potential of AI as a standalone discovery tool.
As powerful as AI already is and will no doubt improve, the results are only as good as the data that is being fed in, and the instruction being applied. As such, all data should ideally be correct and representative of the mineral system that you’re looking at. The refinement, efficiency and predictive powers AI is capable of delivering can only be realised if it is sitting on a credible foundation of geological process.
Drilling exploration holes is expensive, so you need to have confidence in the results that your processes are spitting out. That comes with a bedrock of sound geological practice, data collection, and then processing and application of modern technology.
Technology will continue to evolve and will one day have the power to deliver a true step change in discovery rates when fed with good data. But that day is some way off, yet.
Discovery at depth is our Everest as exploration teams. Better geophysical technologies are leading to better data for modelling, interpretation and predictive exploration. Some of the results we have been seeing give us hope that significant discoveries under cover will become more commonplace.
There is no doubt AI will push the boundaries of how we can look at our datasets and approach targeting. But it is difficult to imagine a time when the need for sound data collection and analysis to feed cutting-edge processing tools becomes unimportant.
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This thought leadership piece is in partnership with the Mining Journal's Future of Exploration.
