University of Tasmania
Browse

File(s) under permanent embargo

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

journal contribution
posted on 2023-05-18, 01:26 authored by Matthew CracknellMatthew Cracknell, Anya ReadingAnya Reading
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

Rights statement

Copyright 2014 Elsevier

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the earth sciences

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC