Abstract Conventional linear iterative methods for magnetotelluric sounding (MT) suffer from considerable limitations related to difficulties in selecting the initial model and local optima. On other hand, conventional intelligent nonlinear exhibit slow convergence low accuracy. In this study, we propose an inversion strategy based on deep learning (DL) belief network (DBN) realise instantaneou...

We develop isometry and inversion formulas for the Segal–Bargmann transform on odd-dimensional hyperbolic spaces that are as parallel as possible to the dual case of odd-dimensional spheres.