GitHub repositories:

PyCPT

This package presents a novel perspective to understand the spatial and statistical patterns of a cone penetration dataset and an automatic approach to identify soil stratification. Both local consistency in physical space (i.e., along depth) and statistical similarity in feature space (i.e., logQt – logFr space or the Robertson chart) between data points are considered simultaneously. The proposed approach is, in essence, consist of two parts: 1) a pattern detection approach using Bayesian inferential framework, and 2) a pattern interpretation protocol using Robertson chart. The first part is the mathematical core of the proposed approach, which infers both spatial pattern in physical space and statistical pattern in feature space from the input dataset; the second part converts the abstract patterns into intuitive spatial configurations of multiple soil layers having different soil behavior types. The advantages of this approach include probabilistic soil classification, and identifying soil stratification in an automatic and fully unsupervised manner. This approach has been tested using various datasets including both synthetic and real-world CPT soundings.

The package is based on the algorithm developed by Wang et al., 2017 and combines Markov Random Fields with Gaussian Mixture Models in a Bayesian inference framework. The recent results have been published in Canadian Geotechnical Journal. The titile of the artical is "A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data".

PyMRF

This package presents a novel stratigraphic stochastic simulation approach, which is developed by integrating a Markov random field (MRF) model and a discriminant adaptive nearest neighbor-based k-harmonic mean distance (DANN-KHMD) classifier into a Bayesian framework. The DANN-KHMD classifier is effective for extracting anisotropic patterns from sparse and heterogeneous spatial categorical data such as borehole logs. The MRF parameters can be initially estimated roughly or customized (if site-specific knowledge is available). Later these parameters can be updated and regularized in an unsupervised manner with constraints from site exploration results in a Bayesian manner. Throughout the learning process, both the soil profile and the MRF parameters are updated in a probabilistic manner. The advantages of the proposed approach can be summarized into four points: 1) inferring stratigraphic profile and associated uncertainty in an automatic and fully unsupervised manner; 2) reasonable initial stratigraphic configurations can be sampled and hence lower the computational cost; 3) both stratigraphic uncertainty and model uncertainty are taken into consideration throughout the inferential process; 4) relying on no training stratigraphy images.