The neural networks applied to geophysical inversion has had limited success due to the need for large training data sets and the lack of generalizability to out-of-sample scenarios. The deterministic regularized inversion often requires a good starting model to avoid possible local minima in highly nonlinear problems. We have developed an inversion-based neural network (IBNN) procedure that combines the advantage of deterministic and neural network inversions in a coupled inversion scheme. The new inversion algorithm is formulated as a constrained problem solved by minimizing an objective function composed of data misfit, neural network misfit, and a coupling model objective function that links the two inversion schemes through a reference model. We investigate two strategies to update the reference model in the coupling model objective function using either a fully-trained network or a progressively trained network that keeps evolving and learning. We extend the analysis of the progressively-trained network inversion without preparing a training data set, which is suitable for most exploration scenarios. We demonstrate the convergence of our procedure in recovering high-resolution resistivity models when applied to synthetic MT data.

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