Predicting Lithology in Mineral Systems

Deep sea minerals exploration and extraction is emerging as an industry on the Norwegian Continental Shelf (NCS). However, the knowledge and available data of the shallow geology of the seabed in the deep sea, the geological setting where mineral deposits are found, is very limited. The primary objective of this project is to develop a machine learning model for predicting the shallow geology in deep sea environments. While the knowledgebase and scientific datasets on marine mineral systems on the NCS is still limited, vast amounts of data for analogous geological settings are available. By developing and training machine learning algorithms on existing analogous data, the geological understanding of deep-sea geology, and thereby also mineral systems, will improve. Deep Sea Minerals on the NCS is however subject to considerable resource and environmental uncertainty and knowledge gaps. This PhD project sets out to close knowledge gaps, and to reduce the uncertainty currently affecting both the industry and the public debate on the topic. Historically, geological data collection on the continental shelf has been driven by the needs of the oil and gas industry in Norway. However, as focus shifts towards sustainability, there is a critical need to prioritize data requirements for green economy initiatives. The research will specifically target the shallow geological zone between the seabed and 200 meters down, transitioning from traditional deep petroleum targets to mineralogical surface and near-surface deposits. The knowledge gained from the Ph.D. project will enable faster and more informed decision-making processes. As such, and in summary, this project aims to enhance the utilization and analysis of an array of data types to facilitate better decision-making in the transition from the oil and gas industry to a greener economy. This will be of value for the emerging Norwegian deep sea mineral industry, an industry poised to be of national and global impact.

Deep sea minerals exploration on the Norwegian Continental Shelf (NCS) is gaining traction, with the first licensing round expected to award licenses in Q1 2025. This emerging industry faces significant scientific, resource, and environmental uncertainties. This project aims to address these knowledge gaps and reduce uncertainties affecting the industry and public debate. The primary goal is to develop a prediction model for shallow lithology in deep-sea environments using machine learning. Although datasets on marine mineral systems on the NCS are limited, extensive analogous geological data exist. Training machine learning algorithms on this data will enhance the geological understanding of deep-sea lithology and mineral systems. As new data from NCS exploration emerges, the model will be refined and validated. The project will leverage various geospatial datasets, including surface data, structural elements, raster data, geophysical datasets, and well data. The research will focus on the shallow lithological zone down to 200 meters below the seabed. A secondary objective is to establish robust data protocols to ensure transparency, integrity, and reliability. As deep-sea mining is an emerging industry, inconsistent data standards hinder effective cross-industry data utilisation. The project will develop methods for combining geomorphological, geological, and geophysical data, promoting innovative data recycling to minimise environmental impact and reduce costs. The PhD candidate will work with Adepth and have access to their data and networks, and data from sources like the Norwegian Offshore Directorate and Geological Survey of Norway. The research aims to publish three peer-reviewed articles on data representation methods, a cross-disciplinary study on machine learning for surface data, and a model for sub-surface data. Overall, the project aims to advance the understanding of deep-sea minerals, improving exploration efficiency and sustainability.