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This chapter addresses a range of topics that hold considerable promise for future developments. We start by considering nested-inversions that allow definition of heterogeneity across a wide range of length scales from local through regional to global. This is followed by discussion of adaptive numerical gridding, exploitation of data redundancy, the development of efficient random sampling methods for inversion, and the use of Hamiltonian Monte-Carlo techniques for efficient searching of high-dimensional spaces.
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