Machine learning algorithms (MLAs) are a powerful group of data-driven inference tools that offer an automated means of recognizing patterns in high-dimensional data. Hence, there is much scope for the application of MLAs to the rapidly increasing volumes of remotely sensed geophysical data for geological mapping problems. We carry out a rigorous comparison of five MLAs: Naive Bayes, k-Nearest Neighbors, Random Forests, Support Vector Machines, and Artificial Neural Networks, in the context of a supervised lithology classification task using widely available and spatially constrained remotely sensed geophysical data. We make a further comparison of MLAs based on their sensitivity to variations in the degree of spatial clustering of training data, and their response to the inclusion of explicit spatial information (spatial coordinates). Our work identifies Random Forests as a good first choice algorithm for the supervised classification of lithology using remotely sensed geophysical data. Random Forests is straightforward to train, computationally efficient, highly stable with respect to variations in classification model parameter values, and as accurate as, or substantially more accurate than the other MLAs trialed. The results of our study indicate that as training data becomes increasingly dispersed across the region under investigation, MLA predictive accuracy improves dramatically. The use of explicit spatial information generates accurate lithology predictions but should be used in conjunction with geophysical data in order to generate geologically plausible predictions. MLAs, such as Random Forests, are valuable tools for generating reliable first-pass predictions for practical geological mapping applications that combine widely available geophysical data.

Item Type:	Refereed Article
Keywords:	machine learning, spatial information, geological mapping, remote sensing, supervised classification, spatial clustering
Research Division:	Earth Sciences
Research Group:	Geophysics
Research Field:	Geophysics not elsewhere classified
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the earth sciences
UTAS Author:	Cracknell, MJ (Dr Matthew Cracknell)
UTAS Author:	Reading, AM (Professor Anya Reading)
ID Code:	92287
Year Published:	2014
Funding Support:	Australian Research Council (CE0561595)
Web of Science® Times Cited:	294
Deposited By:	Earth Sciences
Deposited On:	2014-06-12
Last Modified:	2017-10-25
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