A machine learning model for structural trend fields

Abstract

This work presents a Gaussian process model (a Bayesian derivation of kriging) for the interpolation of structural field data (dip and strike measurements). The structural data are treated as the directional derivatives of a latent potential field. The latent field’s isosurfaces characterize the general structural trend in a region, and the predictive variance can be used as a measure of uncertainty. The model’s parameters are optimized via maximum likelihood, avoiding the need for a variogram analysis. The model is tested using the orientation vectors of metamorphic foliation in meta-volcanic rocks of the Passo Feio Metamorphic Complex, in southern Brazil. An open-source implementation is available.