Publication Name: AEGC 2021
Authors: David Annetts, Juerg Hauser
Date Published: September 2021
Number of Pages: 6
Abstract:
Interpretation of electromagnetic survey data for underlying geology is a common task that is complicated by the volume of data collected in airborne electromagnetic (AEM) surveys. We test a supervised learning approach for AEM data collected in the La Grange groundwater area, Western Australia. We use machine learning to identify the most likely geological setting at each station and use this to derive the probable extent of the seawater interface. We employ standard techniques such as cross validation to benchmark machine learning algorithms such as nearest-neighbour, naive Bayes and support vector networks.