We present the Neuro-Physical Inverter (NPI), a modular, uncertainty-aware framework for geophysical inversion that couples ensemble-based conditioning with constrained residual learning, demonstrated in the 1D magnetotelluric (MT) setting as a controlled testbed.
The framework operates in two stages.
An Ensemble-Conditional Gaussian Process (EnsCGP) conditions a prior ensemble of resistivity models on the observed response, producing a physically admissible reference ensemble.
A residual-learning neural network then predicts targeted corrections to this reference, trained on synthetic data and fine-tuned per station for field application through a physics-coupled objective.
Because an ensemble is conditioned, refined, and propagated through both stages, every estimate carries an associated ensemble spread.
Synthetic experiments show that NPI systematically reduces ensemble-mean error without destabilizing the ensemble.
Applied to broadband MT data from the Gabbs Valley geothermal region (Nevada, USA), NPI reduces the across-station mean misfit over the mid-period band while retaining comparable ensemble spread.
The propagated ensemble yields a factor of uncertainty that serves as an operational measure of constraint within the assumed model class.
Both stages are dimension-agnostic in formulation, and the design principles established here are intended to scale to higher-dimensional parameterizations.