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The Neuro-Physical Inverter (NPI) is a modular, uncertainty-aware framework for geophysical inversion that couples ensemble-based conditioning with constrained residual learning, tested in 1D magnetotelluric (MT) data. An Ensemble-Conditional Gaussian Process generates a physically admissible reference ensemble, which a residual-learning neural network then refines with targeted corrections trained on synthetic data and fine-tuned per station. NPI systematically reduces ensemble-mean error without destabilizing the ensemble, as demonstrated by improved misfit in a real-world geothermal region while retaining comparable uncertainty spread.
Geophysical inversion uncertainty is now quantifiable and reducible by coupling ensemble conditioning with physics-informed neural network residuals.
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.