Classification of Geophysical Images to Map Lithotypes
Supervisors
Research Affiliations
Degree Type/Name
Pre-requisites
Student Support
Collaboration
Dr EJ Holden and Prof Mike Dentith
Centre for Exploration Targeting
Honours - Computer Science, Geology Earth Science or Minerals
Geoscience
Degree in Computer Science or equivalent
The student undertaking this project will work with staff from the
Schools of Earth & Geographical Sciences and Computer
Science & Software Engineering
Collaboration with local mining companies is expected
Skills
This research project provides the opportunities for research students to obtain skills and
experience in the enhancement and interpretation geophysical data, skills highly regarded in
mineral exploration companies, as well as forming a firm basis for a research or academic
career.
Project Description
In geological structures, varying litho-types are layered on top of one another, and
geoscientists recognise their types through texture and colour characteristics. This project is
to automatically identify litho-types of geological structures using a pattern recognition
technique.
Automatic recognition of patterns through the use of multiple image cues such as texture and
colour has been an active area of research in computer vision. In general, a pattern
recognition task is divided into two stages. The first stage is to extract a set of featuresfrom
images that represents a pattern (or an object). There exist numerous feature sets including
geometrical, topological, as well as appearance based features. Appearance-based features
may include a probability distribution function of the pixel intensity or colour distribution, or
wavelet-based texture representation.
The second stage is to recognise these features as a pattern which is modeled in the system.
This can be achieved through a nearest-neighbour classification technique where the system
chooses the closest pattern that has the minimal feature distance between the extracted
feature setand model feature sets. Recognition can also be achieved through the use of
machine learning based techniques such as neural networks and baysian networks. These
systems require a training of model patterns which is then used for classification.
This project will use magnetic images to recognise litho-types by combining a texture feature
extraction technique and a machine learning classification technique. The student is expected
to have the understanding of fundamental concepts of image processing, and to have some
prior experience in programming. This project needs to be implemented in Matlab.
Figure 1. TMI data showing different responses
due to different lithologies.