Higher Degree by Research Application Portal

Project Overview

This PhD research develops novel machine learning approaches for 3D geophysical inversion of gravity and magnetic data, focusing on generating geologically plausible synthetic training datasets and embedding physical laws into the learning framework through physics-informed neural networks (PINNs). The project addresses the fundamental challenge of recovering meaningful subsurface models from inherently non-unique geophysical data.

Background and Motivation

Australia holds world-class endowments of both critical minerals — lithium, cobalt, nickel, and rare earth elements — and major commodities such as iron ore, copper, and gold. Many prospective deposits remain undiscovered at depth in structurally complex environments where standard exploration approaches struggle. Geophysical inversion of gravity and magnetic data is central to deep targeting, but suffers from non-uniqueness: many subsurface models can reproduce the same surface measurements equally well.

A key bottleneck in applying machine learning to this problem is the lack of training data that is both physically consistent and geologically realistic. This project addresses that directly: synthetic geological models generated with tools such as Noddy will form diverse, structurally plausible training datasets spanning scenarios relevant to mineral exploration in Western Australia. PINNs will then embed gravitational and magnetic potential field equations into the inversion process, ensuring recovered models honour established geophysical principles. Extensions to full tensor gravity gradiometry and magnetic remanence characterisation are natural applications of the framework.

Expected Outcomes

This research will deliver novel inversion algorithms for resolving complex geological structures from gravity and magnetic data. The integration of PINNs ensures solutions remain grounded in established geophysical principles while leveraging machine learning capabilities, producing interpretable results that geologists and geophysicists can trust. As the mining industry increasingly targets deeper deposits in complex geological environments, these advanced inversion capabilities become essential for exploration success and will directly support workflows relevant to MinEx CRC and its industry partners.

Dr Vitaliy Ogarko

vitaliy.ogarko@uwa.edu.au

Skills in geophysics and/or mathematics, and programming capability are essential.
Willingness to learn is a requirement. The capability to work in a team and independently and to take initiative will be appreciated.

The research will be based at the Centre for Exploration Targeting (School of Earth Sciences) at the University of Western Australia (Perth, Australia) within a dynamic team of students and researchers specialising in geophysics, mathematics, geology and statistics. The projects will be carried out in close collaboration with researchers from UWA and CSIRO. The PhD candidate will also have the opportunity to collaborate with researchers at the Loop consortium (https://loop3d.org/).

The Doctor of Philosophy (PhD) is a program of independent, supervised research that is assessed solely on the basis of a thesis, sometimes including a creative work component, that is examined externally. The work presented for a PhD must be a substantial and original contribution to scholarship, demonstrating mastery of the subject of interest as well as an advance in that field of knowledge. 

Visit the course webpage for full details of this course including admission requirements, course rules and the relevant CRICOS code/s.