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Machine learning of lineaments from magnetic, gravity and elevation maps

University of British Columbia

Minerals exploration is becoming more difficult, particularly because most mineral deposits at the surface of the earth have been found. While there may be a lot of sensing data, there is a shortage of expertise to interpret that data. This thesis aims to bring some of the recent advances in AI to the interpretation of sensing data. Our AI model learns one-dimensional features (lineaments) from two-dimensional data (in particular, magnetics surveys, maps of gravity and digital elevation maps), which surprisingly has not had a great deal of attention (whereas getting two-dimensional or zero-dimensional features is very common). We define a convolutional neural network to predict the probability that a lineament passes through each location on the map. Then, using these probabilities, cluster analysis, and regression models, we develop a post-processing method to predict lineaments. We train and evaluate our model on large real-world datasets in BC and Australia.

Text

2019-02-28

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/68438

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/