Geological mapping using remote sensing data: a comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information
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.
Funding
Australian Research Council
AMIRA International Ltd
ARC C of E Industry Partner $ to be allocated
Anglo American Exploration Philippines Inc
AngloGold Ashanti Australia Limited
Australian National University
BHP Billiton Ltd
Barrick (Australia Pacific) PTY Limited
CSIRO Earth Science & Resource Engineering
Mineral Resources Tasmania
Minerals Council of Australia
Newcrest Mining Limited
Newmont Australia Ltd
Oz Minerals Australia Limited
Rio Tinto Exploration
St Barbara Limited
Teck Cominco Limited
University of Melbourne
University of Queensland
Zinifex Australia Ltd
History
Publication title
Computers and Geosciences
Volume
63
Pagination
22-33
ISSN
0098-3004
Department/School
School of Natural Sciences
Publisher
Pergamon-Elsevier Science Ltd
Place of publication
The Boulevard, Langford Lane, Kidlington, Oxford, England, Ox5 1Gb