Neuro-physical method improves underground resistivity models with uncertainty estimates
The Neuro-Physical Inverter: A Modular Framework for Magnetotelluric Inversion Coupling Ensemble Conditioning with Residual Learning
Machine Learning
Summary
Figuring out what lies beneath the Earth's surface can be tricky because many different underground models can explain the same surface measurements. The authors introduce a two-step method where first, a known set of possible models is refined based on the data, and then a neural network predicts further improvements. This approach not only improves the accuracy of the underground resistivity models but also keeps track of how uncertain or confident these predictions are. They tested this method with computer simulations and real field data from a geothermal area in Nevada.
What this means in practice
- •For geophysics teams: Enhance underground resistivity models from magnetotelluric data with improved accuracy and uncertainty estimates for geothermal and mineral exploration.
- •For environmental monitoring groups: Use refined inversion methods to better interpret subsurface resistivity variations for groundwater or contamination studies.
Authors
Jae Deok Kim, Sai Ravela, Rob. L. Evans
Abstract
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.