Olivia joined ALS Geoanalytics in January as a Senior Geologist supporting business development in Asia, Europe and Africa. Olivia has over 6 years experience as a remote sensing geologist, specialising in geological mapping and interpretation of satellite images and digital elevation models. Alongside interpretation Olivia’s experience extends to data processing, image interpretation, and data integration for geological applications. In her role at ALS, Olivia supports business development opportunities for ALS' SmartSuite machine learning platform for mineral exploration. Olivia also supports clients from the enquiry and scoping phase to the technical stage of consulting projects.
Transforming Tellus Programme Data into Discovery: A Platform to Support Mineral Exploration Across Ireland
The Tellus Programme has generated an extensive, high-quality geochemical and geophysical dataset across Ireland, providing a significant opportunity to advance mineral exploration through data-driven analysis. This study focuses on southeast Ireland, where Ordovician volcanic sequences within the SW-NE Caledonian corridor host known VMS mineralisation, including the historically significant Avoca and Bunmahon deposits. Using ALS Geoanalytics' SmartSuite™ platform, we integrate geochemical and geophysical datasets from the Tellus Programme to characterise the signature of known VMS mineralisation and identify analogous targets within the intervening, underexplored corridor. SmartCluster™ analyses patterns across multiple geoscience raster input layers simultaneously, revealing spatial domains reflective of underlying geology that are not apparent when interpreting datasets individually. SmartTarget™ is a supervised machine learning workflow that integrates geoscience raster datasets with mineralisation-related training data to generate mineral prospectivity maps. A probabilistic prospectivity map will be generated across this study area in southeast Ireland by learning the relationship between geochemically-defined VMS training targets and supporting geophysical datasets. This study demonstrates the potential of machine learning to unlock exploration value from regional survey programmes at a deposit scale.
