Three-dimensional geological modelling and visualisation are crucial tools within the resources sector, allowing informed exploration and extraction decisions to be made. Geological structures, such as faults and folds, often control mineralisation within systems. Despite their importance, visualising the interactions of these geometries within three-dimensional space continues to prove challenging.

Loop3D offers a Bayesian approach to stochastic 3D geological modelling, through their opensource Python libraries: Loop Structural and Map2Loop. Loop improves the ease and accessibility of modelling by enabling users to create 3D geological models from digital maps produced in programs such as QGIS. An evidence-based, time-aware approach is applied, meaning that data relating to the most recent events is used to constrain the geometries of older features (Grose et. al, 2020). This enables interpolation algorithms to incorporate multi-deformation and fault events into models, which some programs struggle to achieve.

This paper explores the implementation of Loop3D, across two case studies, to interrogate its potential applications in the resources sector. The first case study generated a model from a student field map, whilst the second focuses on using geo-survey data to model poly-deformed terrain.

Evidence based, geologically sound 3D models are key to improving the sustainability of the resources sector. By enhancing our understanding of a geological system, more informed mining strategies can be put in place to increase revenue. Mining more efficiently through targeting higher probability zones with enriched mineralisation has the potential to reduce the bulk tonnage of extracted rock. This immediately drops operating costs (labour and energy), and reduces the production of waste-rock which dilutes profits. Applying innovative mine designs to geological models could produce smaller surface footprints. In turn, minimising ecosystem disruption thus benefiting social and environmental outcomes as well as positively influencing public perception.

As the demand for critical and strategic metals continues to increase, along with concerns regarding environmental impact, there is a need to prioritise a clean and more efficient route for processing these elements. The growing emphasis on green chemistry has led numerous researchers to focus on environmentally friendly solvents for mineral extraction. One such class of solvents are called deep eutectic solvents (DES). They have garnered attention due to their eco-friendly, non-toxic, and bio-degradable properties. These solvents possess comparable physicochemical properties to conventional ionic liquids but are more cost-effective and environmentally friendly.

This study uses the principles of green chemistry for technospheric mining. Technospheric mining involves extracting valuable products from anthropogenic waste materials, such as discarded consumer products and wastes produced in the mining industry, such as tailings, slag, and industrial byproducts. Extraction from these secondary sources would complement the traditional production process and mitigate any geopolitical risks associated with the supply chain of a given commodity. The current industry could adopt green chemistry principles, such as using DES to process rare earth elements from mine wastes.

Extensive research has been dedicated to using DES to extract valuable metals from synthetic materials and minerals. However, limited attention has been given to exploring their potential with primary ores and primary waste materials. To address this knowledge gap, the current study investigates the applicability of DES to extract rare earth elements from mine wastes and understand how they respond to them. The feedstocks targeted for this study include monazite flotation tailings and acid crack and leach residues. This study will serve as a scoping analysis to assess the response of each feed material and its associated mineralogy to the prepared solvents. Furthermore, using green solvents to reprocess tailings helps minimise the environmental impact of processing. Additional processing is relatively easy as the material is already ground and handled.