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.

这项工作提出了一个高斯过程模型（克里金的贝叶斯派生），用于内插结构场数据（倾角和走向测量）。结构数据被视为潜势场的方向导数。潜场的等值面表征了区域中的总体结构趋势，并且预测方差可以用作不确定性的量度。该模型的参数通过最大似然性进行了优化，从而无需进行变异函数分析。使用巴西南部Passo Feio变质带的变火山岩中变质页岩的定向矢量对模型进行了测试。一个开源的实现是可用的。