  In mining, grade control generally focuses on blast hole sampling and the
estimation of ore control block models with little or no attention given to how
the materials are being excavated from the ground. In the process of loading
trucks, the underlying variability of the individual bucket load will determine
the variability of truck payload. Hence, accurate material movement demands a
good knowledge of the excavation process and the buckets interaction with the
environment. However, equipment frequently goes into off nominal states due to
unexpected delays, disturbances or faults. The large amount of such
disturbances causes information loss that reduces the statistical power and
biases estimates, leading to increased uncertainty in the production. A
reliable method that inferences the missing knowledge about the interaction
between the machine and the environment from the available data sources, is
vital to accurately model the material movement. In this study, a twostep
method was implemented that performed unsupervised clustering and then
predicted the missing information. The first method is DBSCAN based spatial
clustering which divides the diggers and buckets positional data into connected
loading segments. Clear patterns of segmented bucket dig positions were
observed. The second model utilized Gaussian process regression which was
trained with the clustered data and the model was then used to infer the mean
locations of the test clusters. Bucket dig locations were then simulated at the
inferred mean locations for different durations and compared against the known
bucket dig locations. This method was tested at an open pit mine in the Pilbara
of Western Australia. The results demonstrate the advantage of the proposed
method in inferencing the missing information of bucket environment
interactions and therefore enables miners to continuously track the material
movement.
