Automated rock recognition is a key step for building a fully autonomous mine. When characterizing rock types from drill performance data, the main challenge is that there is not an obvious one-to-one correspondence between the two. In this paper, a hybrid rock recognition approach is proposed which combines Gaussian Process (GP) regression with clustering. Drill performance data is also known as Measurement While Drilling (MWD) data and a rock hardness measure - Adjusted Penetration Rate (APR) is extracted using the raw data in discrete drill holes. GP regression is then applied to create a more dense APR distribution, followed by clustering which produces discrete class labels. No initial labelling is needed. Comparisons are made with alternative measures of rock hardness from MWD data as well as state-of-the-art GP classification. Experimental results from an actual mine site show the effectiveness of our proposed approach.

This work is motivated by the need to develop new perception and modeling capabilities to support a fully autonomous, remotely operated mine. The application differs from most existing robotics research in that it requires a detailed world model of the sub-surface geological structure. This in-ground geological information is then used to drive many of the planning and control decisions made on a mine site. This paper formulates a method for automatically detecting in-ground geological boundaries using geophysical logging sensors and a supervised learning algorithm. The algorithm uses Gaussian Processes (GPs) and a single length scale squared exponential covariance function. The approach is demonstrated on data from a producing iron-ore mine in Australia. Our results show that two separate distinctive geological boundaries can be automatically identified with an accuracy of over 99 percent. The alternative approach to automatic detection involves manual examination of these data.

Autonomous operation of blast hole drill rigs requires monitoring of drilling parameters known as "Measurement While Drilling" (MWD) data. From these data, rock properties can be inferred. A supervised classification scheme is usually used to map MWD data inputs to rock type outputs given some labeled training data. However, the geology has no definite ground truth that can allow a reliable labeling of the training data, nor is there a clear input-output pair connection between the MWD data and the rock types. In this paper, an adaptive unsupervised approach is proposed to estimate the rock types in a data driven way by minimizing the entropy gradient of the characterizing measure "Optimized Adjusted Penetration Rate" (OAPR). Neither data labeling nor fixed model parameters are required because of the data driven nature of the algorithm. Experimental results illustrate the effectiveness of our solution.

A crucial component of an autonomous mine is the ability to infer rock types from mechanical measurements of a drill rig. The major difficulty lies in that there is not a clear one to one correspondence between the mechanical measurements and the rock type due to the mechanical noise as well as the variety of the rock geology. This paper proposes a novel wavelet feature space projection approach to robustly classify rock types from drilling data with Gaussian Process classification. Instead of applying Gaussian Process classifier directly to the given measurement pieces, a group of wavelet features are extracted from the neighboring region of a specific data point. Gaussian Process classification is then carried out on the new extracted wavelet features. By putting neighboring data points into consideration rather than dealing with each data point individually, the underlying pattern can be better captured and more robust to noise and data variations. Experimental results on synthetic data as well as varied real world drilling data have shown the effectiveness of our approach.

Measurement-while-drilling (MWD) data recorded from drill rigs can provide a valuable estimation of the type and strength of the rocks being drilled. Typical MWD sensors include bit pressure, rotation pressure, pull-down pressure, pull-down rate, and head speed. This letter presents an empirical comparison of the statistical performance, ease of implementation, and computational efficiency associated with three machine-learning techniques. A recently proposed method, boosting, is compared with two well-established methods, neural networks and fuzzy logic, used as benchmarks. MWD data were acquired from blast holes at an iron ore mine in Western Australia. The boreholes intersected a number of rock types including shale, iron ore, and banded iron formation. Boosting and neural networks presented the best performance overall. However, from the viewpoint of implementation simplicity and computational load, boosting outperformed the other two methods.

Analysis of drill performance data provides a powerful method for estimating subsurface geology. While there have been studies relating such measurement-while-drilling (MWD) parameters to rock properties, none of them has attempted to model context, that is, to associate local measurements with measurements obtained in neighbouring regions. This paper proposes a novel approach to infer geology from drill measurements by incorporating spatial relationships through a Conditional Random Field (CRF) framework. A boosting algorithm is used as a local classifier mapping drill measurements to corresponding geological categories. The CRF then uses this local information in conjunction with neighbouring measurements to jointly reason about their categories. Model parameters are learned from training data by maximizing the pseudo-likelihood. The probability distribution of classified borehole sections is calculated using belief propagation. We present experimental results of applying the method to MWD data collected from a semi-autonomous drill rig at an iron ore mine in Western Australia.