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.

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.

This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function. A novel complex mapping is proposed to nonlinearly adjust the data. Optimization of the data transformation parameters is performed in two different manners. Firstly, the complex data mapping parameters and kernel hyper-parameters are selected separately, with the former guided by frequency metrics and the latter under the Bayesian framework. Next, the complex data mapping parameters and kernel hyper-parameters are optimized simultaneously in a Bayesian formulation by creating a new category of "integrated kernel" with the complex data mapping embedded. Experiments using Gaussian Process learning have shown that both methods improve the learning accuracy in either classification or regression tasks, with the complex mapping embedded kernel approach outperforming the separate complex mapping one.

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.