An AI approach to automated magnetic formation mapping beneath cover. Issue 1 (1st December 2019) - Pratt, David A.; McKenzie, K. Blair; White, Anthony S.
Summary: Most regional scale magnetic maps are dominated by the magnetic characteristics of steeply dipping basement units truncated by an unconformity surface. It is easy to demonstrate that 80 to 90% of each total field magnetic anomaly is contributed by this intersecting surface. We approach this problem by mapping the boundaries between contrasting magnetic units along each line in the magnetic survey using the full precision of the line data and 3D information from the magnetic gradient tensor. Additionally, we derive the azimuth of each boundary, depth to the unconformity and magnetic properties of the anomalous units. The segments are overlain on any image such as existing geological maps, satellite imagery, gravity or magnetic imagery to provide a new geological interpretation concept. This method provides a new way to interpret new and old magnetic surveys. Eigenvector analysis of the magnetic tensor and normalised source strength (NSS) are combined with an artificial intelligence (AI) approach to estimate the basement properties. The method is applied to full tensor magnetic survey data or a grid of the total magnetic intensity data is processed using FFT transformations to derive the magnetic gradient tensor. These data are used as input to the pre-trained AI process for calculation of depth, width, azimuth, magnetic susceptibility and magnetisation direction. The rock properties and depth information can be used for 3D visualisation of the unconformity and 2D mapping of the magnetic lithology of the unconformity surface.