  Rapidly estimating the remaining wall thickness (RWT) is paramount for the
non-destructive condition assessment evaluation of large critical metallic
pipelines. A robotic vehicle with embedded magnetism-based sensors has been
developed to traverse the inside of a pipeline and conduct inspections at the
location of a break. However its sensing speed is constrained by the magnetic
principle of operation, thus slowing down the overall operation in seeking
dense RWT mapping. To ameliorate this drawback, this work proposes the partial
scanning of the pipe and then employing Gaussian Processes (GPs) to infer RWT
at the unseen pipe sections. Since GP prediction assumes to have normally
distributed input data - which does correspond with real RWT measurements -
Gaussian mixture (GM) models are proven in this work as fitting marginal
distributions to effectively capture the probability of any RWT value in the
inspected data. The effectiveness of the proposed approach is extensively
validated from real-world data collected in collaboration with a water utility
from a cast iron water main pipeline in Sydney, Australia.
