Anisotropic induced polarization modeling with neural networks and effective medium theory

Abstract

Interpreting three-dimensional induced polarization (IP) data requires rock physics models that reflect the omnipresent anisotropy of the Earth’s crust. The Generalized Effective Medium Theory of Induced Polarization (GEMTIP) can model the IP signatures of rocks with polarizable mineral inclusions. However, it is computationally demanding because it requires numerically solving the depolarization tensors of each inclusion. We aim to streamline GEMTIP simulations by (1) integrating anisotropic background conductivity and triaxial ellipsoidal inclusions in the model and (2) estimating the depolarization tensors with a neural network. We validate the neural network predictions against known solutions for spherical and spheroidal inclusions, and we test our method using data from an actual rock sample. The neural network shows a relative sensitivity of 56% to inclusion shape and 44% to host rock anisotropy and is up to 100,000 times faster than numerical integration. We release the pre-trained neural network implementation as an open-source Python package, thereby providing a new method to interpret the IP signatures of anisotropic rocks.