  Accurately interpreting induced polarization (IP) data that reflects the
inherent anisotropy of the Earth's crust requires anisotropic IP models. The
Generalized Effective Medium Theory of Induced Polarization (GEMTIP) model
effectively simulates the IP signatures of rocks containing polarizable
minerals. A pivotal element of the GEMTIP model is calculating the
depolarization tensor elements, an intensive task for anisotropic rocks because
one must numerically solve six parametric integrals for each mineral inclusion.
This study aims to streamline anisotropic IP simulations by extending the
GEMTIP framework and introducing a machine learning approach to estimate the
depolarization tensors. The theoretical contributions of this research are
two-fold: (1) we augment the GEMTIP model to encompass anisotropic background
conductivity and triaxial ellipsoidal inclusions, and (2) we reformulate the
depolarization integrals to normalize their input and output variables,
facilitating their estimation by neural networks. Validation against analytical
solutions for spherical and spheroidal inclusions corroborates the accuracy of
the neural network. Analyzing the neural network model, we find that the
relationship between chargeability and polarizable inclusion content is
increasingly uncertain for increasingly anisotropic rocks. A similar
observation applies to the relationship between critical frequency and host
rock conductivity. Moreover, the depolarization tensors are, on average, 56 %
sensitive to inclusion anisotropy and 44 % sensitive to host rock conductivity
anisotropy. Remarkably, our neural network drastically accelerates GEMTIP
simulations--up to 100,000 times faster than numerical integration--without
substantially sacrificing accuracy. This advancement is promising for efficient
rock-scale IP modeling in complex and anisotropic geological settings.
