AEM Assist: A national machine learning tool for airborne electromagnetic interpretation and extrapolation

Output Type: Exploring for the Future Extended Abstract

Short Abstract: Airborne electromagnetic surveys are widely used in Australia for mineral exploration, groundwater assessment (i.e. hydro-stratigraphy and water quality) and natural resource management (i.e. salinity assessment). In the last decade, regional surveys have been acquired covering approximately two thirds of the continent and resulting in a large volume of data to interpret. To address this challenge, we have developed a machine learning workflow to assist with the interpretation of AEM conductivity depth sections. ‘AEM assist’ is an open-source machine learning algorithm that allows the user to interpret AEM sections from drillhole observations and/or interpreted segments along the conductivity depth section. AEM assist finds predictive relationships between the training observations (drillhole and/or interpreted sections) and the conductivity value which also includes the first vertical derivative of the conductivity. Due to the non-uniqueness of the conductivity response, we have also built in a suite of supplementary covariates or features to help improve the model prediction. These features include terrain indices, gamma radiometric, surface weathering intensity, distance proxies (e.g., distance from rocks of a known age), climate indices, gravity, and magnetic derivatives. We have built the AEM assist into a national mapping framework to facilitate model interpretation and training anywhere in Australia. Although local training of sections is recommended the national framework provides an opportunity to train a model in one region and predict into another area given similar geological and landscape histories. The AEM assist has the potential to speed up the interpretation of AEM flightline sections with statistical models of interpretation uncertainty. AEM assist can be used to provide a first pass interpretation of a survey area that can later be revised by the domain expert. A feature of AEM assist is that it systematically integrates many datasets that would otherwise be difficult to do from traditional methods.

Citation: Basak S., Wilford J., Wong S.C.T., Ley-Cooper Y. & Ray A., 2024. AEM assist - a national predictive machine learning framework for airborne electromagnetic interpretation and extrapolation. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra, https://doi.org/10.26186/149495