How can machine learning optimize mineral exploration?

1 Target generation

Target generation is the process of identifying and prioritizing areas that have the potential to host mineral deposits. Machine learning can help with target generation by using various data sources, such as geology, geochemistry, geophysics, and remote sensing, to create predictive models that can rank and classify targets based on their probability of mineralization. Machine learning can also help with anomaly detection, which is the identification of features or patterns that deviate from the normal or expected background. Anomaly detection can help discover new targets or refine existing ones by highlighting areas of interest that may otherwise be overlooked.

2 Data integration

Data integration is the process of combining and harmonizing data from different sources and formats, such as maps, reports, databases, and images, to create a comprehensive and consistent representation of the exploration area. Machine learning can help with data integration by using techniques such as data fusion, data mining, and data visualization. Data fusion is the combination of data from different sensors or modalities to enhance the information content and quality. Data mining is the extraction of useful and relevant information from large and complex data sets. Data visualization is the presentation of data in graphical or interactive forms to facilitate understanding and communication.

3 Drill planning

Drill planning is the process of designing and optimizing the location, orientation, depth, and spacing of drill holes to test the targets and evaluate the mineral resources. Machine learning can help with drill planning by using techniques such as optimization, simulation, and uncertainty analysis. Optimization is the process of finding the best solution or configuration for a given objective or constraint. Simulation is the process of creating and running models that mimic the behavior or performance of a system or process. Uncertainty analysis is the process of quantifying and reducing the uncertainty or variability associated with the data, models, or outcomes.

4 Resource estimation

Resource estimation is the process of estimating the quantity and quality of the mineral resources based on the drill data and geological models. Machine learning can help with resource estimation by using techniques such as interpolation, classification, and geostatistics. Interpolation is the process of estimating the values of unknown points based on the values of known points. Classification is the process of assigning the points or regions to different categories or classes based on their characteristics or properties. Geostatistics is the branch of statistics that deals with the spatial analysis and modeling of geoscientific data.

Machine learning can optimize mineral exploration by providing powerful and flexible tools that can handle large and complex data sets, improve data quality and reliability, reduce exploration risk and cost, and enhance decision making and resource management. Machine learning can also enable new discoveries and innovations that can transform the exploration industry and create value for stakeholders .

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?