Magnetic polarizability tensors (MPTs) provide an economical characterisation of conducting metallic objects and can aid in the solution of metal detection inverse problems, such as scrap metal sorting, searching for unexploded ordnance in areas of former conflict, and security screening at event venues and transport hubs. Previous work has established explicit formulae for their coefficients, and a rigorous mathematical theory for the characterisation they provide [1]. The open-source MPT-Calculator software uses hp-finite elements and a Proper Orthogonal Decomposition reduced order modelling (ROM) approach for computing MPT spectral signatures [2, 3] for different object geometries. MPT invariants have then been used as features in machine learning classification of metallic targets [3]. For objects with a high conductivity, or objects that have a high magnetic permeability, the electromagnetic skin depth becomes very small compared to the size of the object, and requires careful numerical treatment for accurate solutions. We will present an approach for designing the number and thickness of prismatic layers combined with p-refinement for this task. We will also present advances in our ROM approach to improve the computational efficiency of our MPT spectral signature computation by exploiting the mathematical construction of MPT coefficients in combination with adaptivity. These advances are demonstrated via the inclusion of realistic examples of common metal detection targets. REFERENCES [1] P. D. Ledger and W. R. B. Lionheart, The spectral properties of the magnetic polarizability tensor for metallic object characterisation, Math. Methods Appl. Sci., Vol. 43 (1), pp. 78–113, 2020. [2] J. Elgy, P. D. Ledger, J. L. Davidson, T. Özdeğer and A. J. Peyton, Computations and measurements of the magnetic polarizability tensor characterisation of highly conducting and magnetic objects, Eng. Comput., Vol. 40 (7/8), pp. 1778–1806, 2023. [3] B. A. Wilson, P. D. Ledger and W. R. B. Lionheart, Identification of metallic objects using spectral magnetic polarizability tensor signatures: object classification, Int. J. Numer. Methods Eng., Vol. 123 (9), pp. 2076–2111, 2022.