Undercover Mineralisation – Identifying mineral enrichments beneath glacial deposits in northeast Scotland utilising machine learning

The University of Aberdeen is an internationally recognised centre for excellence for research addressing the global challenges of energy transition, environment and biodiversity, social inclusion and cultural diversity, health, nutrition and wellbeing, and data and artificial intelligence. Our interdisciplinary research crosses the broad themes of understanding across these global challenges and you can find out more about the University of Aberdeen’s five interdisciplinary challenge areas here. 

In 2024 we are continuing to build a cohort of interdisciplinary postgraduate research students at the University of Aberdeen through the announcement of nine new Interdisciplinary PhD Studentships. Students will join a cohort of fourteen Interdisciplinary Research Fellows and three interdisciplinary postgraduate students appointed in 2023 and 2024, benefiting from a range of challenge-led activities and cross-discipline interactions, which will provide the students with unique opportunities to develop their skills and their interdisciplinary thinking. 

Project Description:

Critical minerals are essential raw materials for the production of technologies required for the Energy Transition. The British Geological Survey have identified northeast Scotland as a ‘potentially prospective’ region for the discovery of critical mineral deposits (Deady et al, 2023). A substantial proportion of bedrock in this region is ‘Undercover’, of a suite of glacial sediments deposited and reworked during multiple Quaternary glaciation, which makes locating the bedrock regions with critical mineral enrichments challenging.

Mineral enrichments in northeast Scotland include lithium-rich pegmatites, nickel+copper (Ni-Cu) magmatic sulphide intrusions and molybdenite veining within the Grampian granites. Current mineral exploration activity in northeast Scotland is focussed on Ni-Cu magmatic sulphides, whereas potential Lithium-bearing deposits are not being investigated.

Geochemical analysis of glacial deposits for mineral exploration is a well-established technique in other countries, including Fennoscandia and Canada (McClenaghan et al, 2000). These methods rely on identifying enrichments in glacial sediments which are proximal to the mineralised bedrock source and then performing targeted exploration in the areas identified. These methods generally do not account for the variable transport of eroded material by glacial processes and/or complex systems of reworking and deposition in distal regions (McClenaghan & Paulen, 2018) and are generally confined to surficial deposits.

Sampling glacial sediments for mineral exploration has not been undertaken extensively in northeast Scotland. This project will develop an interdisciplinary approach to mineral exploration in regions covered by extensive glacial sediments, by conducting fieldwork in the UK and abroad. Proposed field sites beyond Scotland may include Finland, Canada and Norway. The candidate will conduct sampling and glacial landform mapping and flow unit interpretation in well-established and frontier regions, followed by geochemical analysis and interrogation of results using machine learning techniques. There may also be the opportunity for localised geophysical surveys in regions of interest. The aim is to develop and apply new methods in northeast Scotland to identify prospective areas of bedrock mineralisation which have been overlooked due to sediment cover and to determine if machine learning can be utilised to combine these datasets to determine complex glacial sediment transport directions.

The key aims for this project are:

• Conduct glacial sediment sampling and geochemical analyses in known regions of critical mineral enrichment and determine if these enrichments can be identified in the sediments.
• Perform glacial landform mapping at key study sites and combine with satellite and aerial drone imagery to model sediment transport directions, producing a comprehensive database of ice flow indicators.
• Apply machine learning to large volumes of field and geochemical data to test/develop a method for modelling sediment transport and dispersion from bedrock to glacial deposits.

Training will include:

• Mineral systems and ore petrography, geochemical sample analysis, including SEM, pXRF, XRD, ICP-MS and particle size analysis.
• Geomorphological mapping focussing on glacial landforms, assessments of glacial sediments and stratigraphy utilising field and Geographical Information Systems (GIS) and remote sensing (photogrammetry, SAR and optical satellite training) techniques.
• Geophysical techniques, including magnetometry, electrical methods (resistivity, SP), GPR.
• Application of route optimisation algorithms based on machine learning, using open source tools.

Candidate Background:

Essential:

• BSc or equivalent in an Earth Science-related subject
• Demonstrated fieldwork experience (or digital equivalent)
• Good verbal and written communication skills
• Ability to work independently and as part of a wider team

Desirable:

• Independent fieldwork experience
• Keen interest in mineral deposit systems
• Interest in glacial processes/landforms/sediments
• Experience using GIS software
• Experience working with large datasets and/or databases
• Experience or understanding of relevant lab techniques
• Understanding of machine learning and knowledge of one or more computer coding language(s)
• Independent research experience, (eg. Undergraduate dissertation or similar)

Applicants should hold, or expect to achieve a minimum of a 2.1 UK Honours degree (or international equivalent).Candidates with a 2:2 at undergraduate level may be considered if they hold a Commendation or Distinction at masters level or have significant commensurate experience.

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APPLICATION PROCEDURE:

• Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
• You should apply for Geography (PhD) to ensure your application is passed to the correct team for processing (the programme applied for may not be representative of the programme which will be offered to a successful candidate, this is for administrative purposes only)
• Please clearly note the project title and lead supervisor in the respective fields on the application form
• Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.
• Pease provide two academic references with your application.
• Please note: you DO NOT need to provide a research proposal with this application
• If you require any additional assistance in submitting your application or have any queries about the application process, please don’t hesitate to contact us at [email protected]