A machine learning model for structural trend fields

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. •A potential field is modeled from orientation data alone with the Gaussian process.•Structural surfaces correspond to isovalues of the modeled field.•Covariance parameters are trained through maximum likelihood.•Open-source implementation available.