The Spatially Constrained Bayesian Network (SCB-Net) is a deep learning approach that generates accurate lithological predictions while quantifying uncertainty, by effectively integrating auxiliary data and sparse field observations.
The study compares five roughness descriptors for LiDAR-derived digital elevation models, highlighting the importance of multiple descriptors in characterizing terrain surface roughness.
The author presents a parallel implementation of the Vecchia approximation technique, utilizing batched matrix computations on contemporary GPUs to speed up evaluating the log-likelihood function.