MONITORING-WHILE-DRILLING FOR OPEN-PIT MINING IN A HARD ROCK ENVIRONMENT:

Transcription

1 MONITORING-WHILE-DRILLING FOR OPEN-PIT MINING IN A HARD ROCK ENVIRONMENT: An Investigation of Pattern Recognition Techniques Applied to Rock Identification by Natalie Christine Macerollo Beattie A thesis submitted to the Department of Mining Engineering in conformity with the requirements for the degree of Master of Science (Engineering) Queen s University Kingston, Ontario, Canada April, 2009 Copyright Natalie Beattie, 2009

2 Abstract This thesis investigated the abilities of artificial neural networks as rock classifiers in an open-pit hard rock environment using monitoring-while-drilling (MWD) data. Blast hole drilling data has been collected from an open-pit taconite mine. The data was smoothed with respect to depth and filtered for non-drilling data. Preliminary analysis was performed to determine classifier input variables and a method of labelling training data. Results obtained from principal component analysis suggested that the best set of possible classifier input variables was: penetration rate, torque, specific fracture energy, vertical vibration, horizontal vibration, penetration rate deviation and thrust deviation. Specific fracture energy and self-organizing-maps were explored as a means of labelling training data and found to be inadequate. Several backpropagation neural networks were trained and tested with various combinations of input parameters and training sets. Input sets that included all seven parameters achieved the best overall performances. 7-input neural networks that were trained with and tested on the entire data set achieved an average overall performance of 81%. A sensitivity analysis was performed to test the generalization abilities of the neural networks as rock classifiers. The best overall neural network performance on data not included in the training set was 67%. The results indicated that neural networks by themselves are not capable rock classifiers on MWD data in such a hard rock iron ore environment.

3 Acknowledgements Working on this thesis has been an invaluable experience that has helped me grow in many ways. I would like to express sincere gratitude towards my supervisor, Dr. Laeeque Daneshmend, for his support, guidance, advice, patience and wisdom during the past two years. I would also like to acknowledge the input of Dr. Jonathan Peck, former Head of the Department of Mining Engineering, in formulating the objectives and methodology for this research. As well, I would like to thank the rest of the professors and staff in the Mining Department, especially Dr. Garston Blackwell, Dr. Takis Katsabanis and the office staff for easing my transition into a new department and discipline. I would like to acknowledge Mr. Ted Branscombe and Mr. Dan Lucifora for their part in collecting and organizing the data analyzed in this thesis. I would also like to thank Mr. Branscombe for his shared knowledge on the data and MatLab. I would like to acknowledge the financial support provided by the W.W. King Fellowship and the J. J. Denny Graduate Fellowship. Finally, I would like to thank my family and friends for their continuous encouragement, support and love throughout the development of this thesis. They have been there for me through good times and bad, and I hope they know how much I appreciate it. This research was partially funded by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC). ii

4 Contents Abstract...i Acknowledgements... ii Contents... iii List of Tables... viii List of Figures...ix Nomenclature...1 Chapter 1 Introduction Monitoring-While-Drilling (MWD) Problem Definition and Objectives Thesis Overview...5 Chapter 2 Background and Literature Review Principal Component Analysis (PCA) Neural Networks Supervised Neural Networks Structure: Feedforward Networks Supervised Neural Networks Training: Backpropagation Unsupervised Neural Networks: Simple Competitive Learning...15 iii

5 2.2.4 Unsupervised Neural Networks: Self-Organizing Maps (SOM) Monitoring-While-Drilling (MWD) Measured Drill Variables Specific Fracture Energy (SFE) MWD for Identification of Geology MWD with Neural Networks for Identification of Geology...29 Chapter 3 Hard Rock, Open-Pit Data Set Geological Context of Data Set Composition of Data Set Monitored Drill Response Drill Hole Reports Preprocessing of Data Set Sample Set...40 Chapter 4 Investigation of Specific Fracture Energy Methodology for Investigation of SFE Behaviour of SFE in the Hole Histogram of SFE in Sample Set...49 iv

6 4.4 Comparison of SFE in Different Types of Rock Summary and Conclusions...52 Chapter 5 Investigation of Specific Fracture Energy Principal Component Analysis Methods PCA1: Horizontal Vibration, Vertical Vibration, Thrust, Torque, Penetration Rate, Rotation Speed, Penetration Rate Set Point, Rotation Speed Set Point and Thrust Limit PCA2: Thrust, Torque, Penetration Rate, Rotation Speed, Penetration Rate Set Point, Rotation Speed Set Point and Thrust Limit PCA3: Thrust, Torque, Penetration Rate, and Rotation Speed PCA4: Thrust, Torque, Penetration Rate, Rotation Speed and Deviations PCA5: Vibrations, Torque, Penetration Rate, Penetration Rate Deviation, Thrust Deviation and SFE PCA Summary and Discussion...61 Chapter 6 Investigation of Self-Organizing Maps (SOM) SOM Methods and Design SOM1: Penetration Rate and Torque SOM2: Penetration Rate, Torque and SFE...70 v

7 6.4 SOM3: Penetration Rate, Torque, SFE, Vertical Vibration and Horizontal Vibration SOM4: Penetration Rate, Torque, SFE, Penetration Rate Deviation, and Thrust Deviation SOM5: Penetration Rate, Torque, SFE, Vertical Vibration, Horizontal Vibration, Penetration Rate Deviation and Thrust Deviation SOM Summary and Conclusions...81 Chapter 7 Backpropagation Artificial Neural Networks Applied to Classification of Rock Type Backpropagation Artificial Neural Networks Methods and Design Backpropagation Neural Network Input Parameters Backpropagation Neural Network Outputs Backpropagation Neural Network Training Sets Evaluation of Backpropagation Neural Network Performance Backpropagation Neural Network Design Backpropagation Neural Networks Results: Comparison of Input Parameters Backpropagation Neural Networks Results: Sensitivity Analysis on Size of Training Set...94 vi

8 7.4 Backpropagation Neural Networks Summary and Conclusions...96 Chapter 8 Conclusions and Recommendations for Future Work Primary Contributions Conclusions Recommendations for Future Work References Appendix A: Sample Sets vii

9 List of Tables Table 1: Drill variables and their respective recorded signals...37 Table 2: Mean, variance and range in SFE for various geologically distinct rock types...51 Table 3: Composition of the entire data set by rock type...86 Table 4: Comparison of both overall and rock type performances of backpropagation neural networks trained on the entire data set with different input parameters...90 Table 5: Comparison of incorrect classifications of backpropagation neural network trained on the entire data set with different input parameters...90 viii

10 List of Figures Figure 1: Example of PCA on a two-dimensional data set...7 Figure 2: Schematic of a basic feedforward artificial neural network...10 Figure 3: Visualization of the simple competitive learning algorithm...17 Figure 4: Diagrams of rectangular and hexagonal SOM topologies...19 Figure 5: Stratigraphy of the Biwabik Formation (after Gunderson and Schwartz, 1962)34 Figure 6: SFE and estimated rock transitions vs. depth for hole 090_C05 (07089)...45 Figure 7: SFE and estimated rock transitions vs. depth for hole 061_B14 (07096)...46 Figure 8: SFE and estimated rock transitions vs. depth for hole 048_C40 (07103)...46 Figure 9: Plot of blastability index and rock type verses depth for blast hole EZ-5314 from the Fording River coal mine...49 Figure 10: Histogram of SFE values in sample set...50 Figure 11: Principal component loadings of the first two principal components from PCA Figure 12: Principal component loadings of first two principal components from PCA2 57 Figure 13: Principal component loadings of first two principal components from PCA3 58 ix

11 Figure 14: Principal component loadings of first two principal components from PCA4 59 Figure 15: Principal component loadings of first two principal components from PCA5 61 Figure 16: Log lithology of hole 090_C05 (07089)...67 Figure 17: Plot of SOM1 - penetration rate vs. torque...69 Figure 18: SOM1 classification of hole 090_C05 (07089)...69 Figure 19: Plot of SOM2 penetration rate vs. SFE...71 Figure 20: Plot of SOM2 penetration rate vs. torque...71 Figure 21: Plot of SOM2 classification of blast hole 090_C05 (07089)...72 Figure 22: Scatter plot of SOM3 - penetration rate vs. vertical vibration...74 Figure 23: Scatter plot of SOM3 - horizontal vibration vs. SFE...74 Figure 24: Scatter plot of SOM3 - penetration rate vs. torque...75 Figure 25: SOM3 classification of blast hole 090_C05 (07089)...75 Figure 26: Scatter plot of SOM4 - torque vs. penetration rate deviation...77 Figure 27: Scatter plot of SOM4 - penetration rate vs. thrust deviation...77 Figure 28: Scatter plot of SOM4 - penetration rate vs. torque...78 Figure 29: SOM4 classification of blast hole 090_C05 (07089)...78 Figure 30: Scatter plot of SOM5 - vertical vibration vs. penetration rate deviation...80 x

12 Figure 31: Scatter plot of SOM5 - penetration rate vs. torque...80 Figure 32: SOM5 classification of blast hole 090_C05 (07089)...81 Figure 33: Plot of number of hidden nodes vs. mse for 7-input backpropagation neural network...89 Figure 34: Classification of blast hole 090_C05 (07089) by 7-input back propagation neural network trained with entire data set...92 Figure 35: Classification of blast hole 061_B14 (07096) by 7-input backpropagation neural network trained with entire data set...93 Figure 36: Classification of blast hole 048_C40 (07103) by 7-input backpropagation neural network trained with entire data set...94 Figure 37: Plot of performance vs. training set size to test set size ratio for 7-input backpropagation neural network...96 xi

13 Nomenclature F J kpa Thrust (N) Joules Kilopascals LWD Logging-while-drilling m mse Metres Mean squared error MWD Monitoring-while-drilling N N Nm R rpm s SFE Newtons Rotation speed (rpm) Newton-metres Penetration rate (m/s) Revolutions per minute Seconds Teale s Specific fracture energy SOM Self-organizing maps T Torque (Nm)

14 Chapter 1 Introduction 1.1 Monitoring-While-Drilling (MWD) Monitoring-While-Drilling (MWD) (also known as Logging-While-Drilling - LWD) is a tool that has been developed in an effort to increase efficiency in the mine. As the name implies, MWD involves the recording of drill variables while drilling in order to obtain information about the drilling environment. It has previously been implemented in both underground and surface mines. Several applications for MWD have been explored, including ore boundary determination, rock strength estimation, drill performance optimization, blast hole design optimization, drill automation, and bit wear monitoring. There are a number of benefits associated with MWD. Most of these benefits result from access to real-time data, leading to near-real-time information, which can be used to improve decision making at both the planning and operations levels. A major attraction of MWD is the potential for obtaining such information much more quickly, comprehensively, and cheaply than alternative means such as geophysical logging. One example of these advantages is that MWD records measurements made while the rock is in situ, which provides a better estimate of rock strength than uniaxial or point 2

15 load testing of prepared rock samples (Boonen, 2003). Better estimates of rock strength have the potential to result in more accurate identification of ore boundaries and rock structure, which would aid in blast and drill optimization and mill preparation. On a shorter time scale, real-time data analysis may have the potential to indicate the degree of bit wear, enabling predictive bit replacement and reducing maintenance time and costs. The main obstacles for MWD have been quality of data, recording frequency, and analytical techniques. Technological advances have helped to overcome the first two obstacles. Improved data recorders are providing more reliable data at a higher frequency, presenting a better idea of what is occurring down hole (Smith, 2002). However, the analytical techniques still require further study. This is a major hurdle, since many of the benefits associated with MWD cannot be realized until appropriate techniques for analysis of the drill variables are developed. 1.2 Problem Definition and Objectives Before the mining process can be implemented, an understanding of the rock types to be fragmented and excavated, along with their physical properties, is required. This understanding is necessary for ore boundary identification and safety, and provides other benefits including blast optimization, digging optimization, and mill preparation. Unfortunately, rock identification is a lengthy and expensive process. Holes must be drilled and logged, then a geologist must interpret the logged data. To lessen the expense mines will often log only a few widely spaced holes and interpolate ground conditions 3

16 between the logged holes, leading to inaccuracies (Schunnesson, 1997).A more accurate method of identifying geology would be beneficial. MWD is a possible solution if the data can be interpreted correctly. This thesis investigates the ability of pattern recognition techniques to identify geology in a hard rock open-pit mine with MWD data. Specifically, neural networks are explored as analytical tools to classify rock types from open-pit taconite MWD data. The main objectives of this investigation are as follows: 1. Identify effective, geology-dependent, neural network input parameters through Principal Component Analysis, previous work on drill parameters, and selforganizing maps for MWD data from a hard rock environment. 2. Investigate the abilities of specific fracture energy and self-organizing maps as methods of labelling MWD data by rock type in a hard rock environment. 3. Investigate the abilities of backpropagation artificial neural networks as rock classifiers of MWD data from a hard rock environment. 4. Compare the MWD neural network results from a hard rock, open-pit environment to previous results in soft rock environments to identify and explain differences in the classifier behaviour. 4

17 1.3 Thesis Overview Background information on the analytical methods used in this thesis such as principal component analysis and artificial neural networks, as well as an overview of past work on MWD are provided in Chapter 2. Chapter 3 describes the data set and the geological environment in which it was collected. Chapter 4 is an investigation of specific fracture energy that examines its characteristics and abilities to label MWD data by rock type. A thorough principal component analysis exploring which drill parameters best describe the geology is detailed in Chapter 5. Chapters 6 and 7 discuss the results from classifiers based on self-organizing maps and backpropagation neural networks respectively. Chapter 8 presents the overall conclusions and recommendations for future work. 5

18 Chapter 2 Background and Literature Review This chapter provides background information on both the analysis techniques and main topics in this thesis. Mathematical methods used in the analysis, including principal component analysis and artificial neural networks, are explained in sections 2.1 and 2.2. Section 2.3 discusses MWD and previous work on the subject. 2.1 Principal Component Analysis (PCA) When collecting experimental data, each data point is defined by its measured variable values. In other words, the measured variables form the coordinate system, or basis, for the experimental data. Often when a process that is not well understood is being observed, the measured variables are correlated and do not provide the most effective perspective for observation and interpretation. Principal Component Analysis (PCA) aims to find a more efficient basis for determining which variables convey the most information about a process. Figure 1a shows a simple example of PCA on a two-dimensional data set. The original basis for the experimental data consists of the correlated variables x and y. PCA has found a new basis for the data set, consisting of the uncorrelated vectors PC1 and PC2. The new 6

19 basis has the same dimension as the old basis yet differs in orientation (Smith, 2002). The axes of the new basis, PC1 and PC2, are known as the first and second principal components. PC1 can adequately describe the data by itself, as it is the direction in which the most variation occurs. This is indicated by its length. PC2 remains almost constant throughout the data set, and can be neglected, as shown in Figure 1b. (Shlens, 2005). Figure 1: Example of PCA on a two-dimensional data set PCA is performed as follows (Shlens, 2005): 1. Organize data in the m by n matrix X where m is the number of variables and n is the number of observations. 7

20 2. Normalize and scale the data in X. 3. Calculate the covariance matrix: C x 1 n 1 T = XX. This is a square, symmetric matrix. The values along the diagonal are the variances of the variables; the rest of the values are the covariances among the variables. 4. Calculate eigenvalues and eigenvectors of the covariance matrix. Each eigenvector-eigenvalue pair forms a principal component: eigenvectors are the directions and eigenvalues are the magnitudes. The principal components are ranked in descending order by magnitude. The dimension of a data set can be reduced, with little loss of information, by removing the lowest ranking eigenvectors. The new basis also reveals patterns in the measured variables (Smith, 2002). The principal components are simply linear combinations of the original measured variables. Each measured variable has a corresponding coefficient within each principal component called its principal component loading (Davis, 2002). Comparison of the principal component loadings in the top ranking principal components indicates which measured variables have the most effect on the data set. Interpretation of PCA results depends on a number of factors - most importantly the priorities of the analyst. PCA only indicates which measured variables are responsible for the most variation. It does not take into consideration the specific purpose of the analysis. 8

21 For example, in this study the PCA results suggest which variables are responsible for variations in drill variable response - this may not correspond to variations in geology. 2.2 Neural Networks Artificial neural networks are systems that have been inspired by biological nervous systems. There are many types of neural networks which serve a variety of purposes and applications including pattern recognition, identification, classification, speech, vision, and control systems. The common characteristic of all neural networks is that they are trained to perform a specific function. All that is needed is a training set for the network to learn how to perform its function. Once the network is trained, inputs are presented to the network and a set of outputs is produced. No physical understanding of the relationships between the inputs and the outputs is needed (Mehrotra et al, 2000). This can be useful for complex systems such as drilling, where it is difficult to consider all of the interactions at once. Neural networks are often classified by their training processes. There are two main types: supervised learning and unsupervised learning. The main difference between the two is the composition of the training set. Supervised networks require both a training input set and a corresponding set of target outputs to which it can compare its performance. Unsupervised networks only require a set of training inputs (Mehrotra et al, 2000). 9

22 The rest of this section describes both supervised and unsupervised neural networks in more detail. While there are many different structures and processes for neural networks, the focus is on those applied in this thesis Supervised Neural Networks Structure: Feedforward Networks The feedforward neural network is the most common type of supervised neural network (Carpenter, 1989). An example of the structure of a basic feedforward neural network with p inputs and r outputs is shown in Figure 2. Figure 2: Schematic of a basic feedforward artificial neural network Figure 2a shows the structure of an entire feedforward neural network. It consists of layers of nodes (circles), whose outputs are n ij where j denotes the node position in layer i. The first layer is the input layer; it receives the inputs. The number of nodes in the input 10

23 layer is equal to the length of the input vector. The next layer is the hidden layer. The number of hidden layers can vary; some networks do not have a hidden layer while others have multiple hidden layers. For simplicity this example has only one hidden layer. The number of hidden layers and the number of nodes in the hidden layer(s) depend on the complexity of the network function. The final layer is the output layer and it computes the final outputs. The number of nodes in the final layer is equal to the desired length of the output vector. The information introduced to the network is always moving forward from the first layer to the final layer, hence the name feedforward neural network (Mehrotra et al, 2000). Figure 2b shows the interaction between layer i and node a in layer k. The layers are connected by weights, as indicated by the arrows in the schematic. The output from node j in layer i is multiplied by an assigned weight w ik ja, then passed on to node a in layer l. Node n ka sums all of its received weighted outputs and calculates its output as: c ik n = f ( [ w n ]) 1 ka j= a ja ij where f is the transfer function. The role of f is to determine which pieces of information are fed forward in the network (Mehrotra et al, 2000). It can be thought of as the on or off switch for the node n ka. There are several options for the choice of f, including the step function, identity function, ramp function, sigmoid function, piecewise linear function, or 11

24 Gaussian function. (Mehrotra et al, 2000) Ideally the transfer function is differentiable and saturating for training purposes. (Hornik, 1990) To summarize, the feedforward neural network process is as follows: 1. An input is introduced to the input layer. 2. The input layer passes its weighted inputs to the first hidden layer. 3. The hidden layer sums its weighted inputs, passes the sum through its transfer function, and presents its outputs to the next layer. This is repeated for each of the hidden layers. 4. Once the output layer has been reached, it calculates the sum of its weighted inputs and passes the sum through its transfer function to produce the final output Supervised Neural Networks Training: Backpropagation Before the feedforward process can be implemented, the network must be trained to determine the optimal weights. The weights are extremely important in determining the network function. Initially, weights are randomly assigned. During the training process, the network learns which weights work best for its purpose and adjusts them accordingly (Mehrotra et al, 2000). As mentioned above, supervised neural networks require a set of target outputs in their training sets. The learning process involves presenting the training inputs to the network, calculating outputs with the current weights, comparing the network output to the desired outputs, then changing the weights accordingly. The learning algorithm must determine 12

25 which weights to change and what the change in weight should be. The most common type of supervised learning algorithm for feedforward networks is backpropagation (Carpenter, 1989). The goal of backpropagation is to minimize the error function between the calculated output and the desired output. Usually the error function is mean squared error (mse), although other options such as mean absolute error can also be used. The error function is minimized with the gradient descent rule, which states that the weight change should be in the direction of the negative gradient of error with respect to weight, -. The algorithm evaluates this gradient for each weight in the network. Essentially, backpropagation algorithms search for the global minimum in the error function by adjusting weights (Mehrotra et al, 2000). There are many backpropagation training algorithms and they all rely on the basic backpropagation equation for a specific weight change shown in the equation below: ik E Δw jl = α 2 w ij where is the weight connecting node j in layer i to node l in layer k, α is the learning rate, and E is the error function. 13

26 While the gradient determines the direction of the weight change, the learning rate determines the magnitude of the weight change. A large value of α allows for quick learning but may cause the weights to oscillate and not converge. A small value of α causes slow learning (Mehrotra et al, 2000). Backpropagation has some limitations. The most significant are over fitting and local minima. Over fitting occurs when the network cannot generalize, or perform well on all ranges of inputs, not just on those used in the training set. The goal of backpropagation is to find the global minimum of the error function, yet there is always the danger of getting trapped in a local minimum. Excessive training can cause the network to memorize the training set and reduce its ability to adapt to new inputs. Over fitting can be avoided by selecting the appropriate size of network and evaluating the network performance while training the network. Limiting the number of nodes in the hidden layers will not allow the network enough resources to memorize the data. Nonetheless, selecting the number of nodes must be done with care as too few will not allow the network enough power to perform its designated task. The performance of the network can be evaluated during the training process by dividing the training set into a training set and a testing set. The normal training algorithm is implemented with only the training set. After each weight update, the algorithm calculates the error with respect to the testing set. Once the testing set error becomes 14

27 significantly worse than the training set error, the training algorithm terminates (Sietsma and Dow, 1991). A local minimum could be substantially different from the global minimum. One wellknown solution for dealing with local minima is momentum. It takes into consideration the previous few weight changes, allowing it to respond to recent changes in the error function as well. This causes the algorithm to ignore small changes in the error surface and avoid small local minima (Mehrotra et al, 2000) Unsupervised Neural Networks: Simple Competitive Learning Unsupervised neural networks do not require a set of target outputs. They are practical for tasks such as cluster analysis, vector quantization, probability density approximation and feature extraction (Mehrotra et al, 2000). This training process has been described as learning without being taught. Since the network does not have a set of desired outputs for guidance it must use other tools to learn, such as distance comparisons. A well-known foundation for unsupervised training algorithms is simple competitive learning (Mehrotra et al, 2000). Simple competitive learning is performed with a two layer network. The input layer is connected directly to the output layer with weights. The nodes in the output layer are also interconnected with weights. The number of input nodes is equal to the length of the input vector. The number of output nodes depends on the desired number of classes or clusters (Kohonen, 1982). 15

28 Each output node has an associated weight vector, w k = [w 1k,w 2k,,w pk ]. The w ik are the weights connecting node i in the input layer to node k in the output layer. Simple competitive learning uses a distance comparison to determine which weight vector is closest to the input. Each output node calculates the distance from the input vector x to its weight vector w k, d(x,w k ). (Kohonen, 1982) Any measure of distance can be used for the distance function d. Popular choices are the Hamming distance (for binary vectors) or Euclidean distance (Mehrotra et al, 2000). The interconnections in the output layer allow the output nodes to compare their distances. The output node with the smallest distance is the winning node. Essentially, each output node is the center of a cluster, or class, of inputs. The winning output is the center of the cluster to which the input belongs. (Kohonen, 1982) To summarize, the procedure followed by a simple competitive learning network is as follows: 1. An input is introduced to the input layer. 2. The input layer passes its weighted inputs to the output layer. 3. The output layer calculates the distance between the input and its weight vector. 4. The output nodes compare their calculated distances. 5. The output node with the smallest distance is the winning node and outputs one while the rest output zero. 16

29 The winning node is moved closer to the input by adjusting its weight vector while training. The rest of the weight vectors remain the same. The idea is that the winning node is moving towards the centre of a cluster of inputs, as shown in Figure 3. Figure 3: Visualization of the simple competitive learning algorithm In Figure 3, the dark node is the winning node. It is located closest to the input indicated by the star. The simple competitive learning algorithm moves the fourth node even closer to the input. This is known as the Kohonen Learning Rule, which is summarized in the equation below (Kohonen, 1982): m 2i m ( t + 1) = m 2i 2i ( t) + α( t)[ x( t) m ( t) i c 2i ( t)] i = c i c 3 where m 2c is the winning weight vector, m 2i are the weight vectors, α(t) is the learning rate, and x(t) is the input vector. The simple competitive learning rate serves the same role 17

30 as the backpropagation learning rate: it determines the magnitude of the weight change. Large learning rates lead to rapid learning with the danger that the weights will not converge. Small learning rates cause slow learning. The distance between the input vector and the weight vector determines the direction of the weight change. The learning algorithm terminates when the weights converge (Kohonen, 1990) Unsupervised Neural Networks: Self-Organizing Maps (SOM) Kohonen (1982) developed self-organizing maps (SOM) as an extension of simple competitive learning. SOM incorporate the concept of a neighbourhood of nodes. The idea is for neighbouring nodes to react similarly to the same input. In other words, adjacent nodes have similar weight vectors. This preserves the topology of the network and allows for distribution analysis along with cluster analysis. In comparison with simple competitive learning networks, fully trained SOM output nodes indicate the distribution of the inputs not just the locations of their cluster centers. SOM follow the same procedure as a simple competitive learning network. The structure of an SOM is also similar to the structure for a simple competitive learning network. There are still two layers, with the input layer directly connected to the output layer through the weight vectors. SOM differ from simple competitive learning networks in the types of interconnections in the output layer. The connections between the output nodes depend on whether or not they are in the same neighbourhood (Kohonen, 1990). 18

31 The shape of the neighbourhood is dictated by the predetermined topology. Some examples of SOM topologies are rectangular or hexagonal, as shown in Figure 4. Solid lines indicate neighbourhood connections and dashed lines indicate options for neighbourhood boundaries, centered at the dark nodes. The size of the neighbourhood is variable. It is recommended that the neighbourhood size decrease with time during the training process (Mehrotra et al, 2000). Figure 4: Diagrams of rectangular and hexagonal SOM topologies When training, all of the weight vectors in the winning node s neighbourhood are updated instead of just the winning node. Updating nodes other than the winner results in a more 19

32 robust network (Kohonen, 1982). The SOM training algorithm is shown in the equation below: m 2i m ( t + 1) = m 2i 2i ( t) + α( t)[ x( t) m ( t) i c 2i ( t)] i N( m 2c otherwise ) 4 where N(m 2c ) is the neighbourhood of the winning node. 2.3 Monitoring-While-Drilling (MWD) Measured Drill Variables The most common measured drill variables are time, depth, torque, thrust, penetration rate and rotation speed. Other variables such as vibration, flushing medium pressure or flow rate are sometimes included. They are usually divided into two groups: independent drill variables and dependent drill variables. Thrust and rotation speed are considered independent variables since they are usually controlled by the operator. Penetration rate and torque are considered dependent variables: most previous work has found that they are not easily controlled by the operator and more reflective of geology as discussed in the following sections Specific Fracture Energy (SFE) Efforts have been made to combine measured rock variables to create new variables that are indicative of the properties of the rock being drilled. Teale s Specific Fracture Energy 20

33 (SFE) is an example of such an effort that is found in most literature regarding rock identification through MWD. In this thesis, SFE always refers to Teale s Specific Fracture Energy. Teale (1965) calculated energy consumption as a function of the measured mechanical variables of the drill. He defined SFE as the work done per unit volume of excavated rock. Power transmitted by the drill consists of two components, rotary power and thrust (feed) power, and is calculated as shown in the equation below W π = FR + NT 5 30 where W is work per unit time (power in units of J/s), F is thrust (N), R is the penetration rate (m/s), N is rotation speed (rpm), and T is torque (Nm). Teale calculates volume drilled per unit time as: V = AR 6 where V is volume per unit time (m 3 /s) and A is the area of the drill hole (m 2 ). SFE is calculated by dividing power by volume per unit time: F π NT SFE = + 7 A 30A R where SFE is specific fracture energy (J/m 3 ). The energy consumed while drilling does not solely depend on rock type. The amount of grinding that occurs while drilling also has an effect on the energy consumed. Drilling 21

34 that results in smaller rock fragments requires more energy. As the rock fragments increase in size, the amount of energy consumed decreases until it approaches a constant. There is a minimum amount of SFE required to break a specific volume of rock, which is achieved when drilling at maximum mechanical efficiency (Teale, 1965). Teale concluded that minimum SFE is constant in all rock types and that it correlated well with compressive rock strength. There is evidence that other factors influence SFE. Teale found that bit geometry may have some influence on SFE. Rabia (1982) concluded that factors such as size of hole, mode of rock breakage and bit type all affect SFE. Both of these conclusions are worth consideration MWD for Identification of Geology There have been several studies on rock identification through MWD as discussed in the following sections. These studies have been performed in both open-pit and underground mines, in various types of rock, and for both rotary and percussive drills. Most previous works concluded that rock type could be identified through MWD, and that penetration rate, torque and SFE best reflect geology. Penetration Rate Penetration rate is the most popular choice as a variable for rock classification. It is often the variable of most interest in MWD studies, and in the past it has been used as a single variable identifier. Simple physics dictates that the drill will penetrate more rapidly in 22

35 softer rocks, which leads to the conclusion that rate of penetration is an indication of rock strength. Penetration rate is also selected for convenience; it is easy to measure and analyse with little computer power. In their drill monitoring study at a surface coal mine in western Canada, Scoble, Peck and Hendricks (1988) found that changes in penetration rate were good indications of coalwaste and waste-coal interfaces. When comparing drill variable response to geophysical logs, they observed that penetration rate decreased with rock strength. They noted that, especially in hard rock, changes in properties were best reflected by torque and penetration rate. Scoble et al also determined that changes in penetration rate can reflect changes in the other measured variables. Schunnesson (1997) and Yue et al (2003) agreed that penetration rate was indicative of rock properties. For his doctoral thesis, Schunnesson performed an underground percussive drill monitoring study in a copper mine and two iron ore mines in Sweden. He focused on identifying ore boundaries and weaknesses in the rock structure through both the normalized magnitude and variability of drill variables. He constantly stressed the importance of normalizing drill variables with respect to each other and other external influences. In the copper mine, he found that the normalized magnitude of penetration rate, along with the normalized magnitude of torque, could indicate rock homogeneity, while penetration rate variability along with torque variability could indicate rock hardness. He stated that in less complex geologies these variables could be used to 23

36 directly classify rock type. In the iron ore mines, he was able to construct a model that could predict iron ore percentage in the rock based on penetration rate and torque. Yue et al used MWD to identify subsurface volcanic weathering profiles in China. They concluded that changes in penetration rates indicated changes in the volcanic weathering and decomposition grades in the ground. Kahraman et al (2003) monitored drill variables in an open-pit limestone quarry and three motorway construction sites. They used linear regression to correlate penetration rate with various measures of rock strength, the most prominent being uniaxial compressive strength (r = 0.91), Brazilian tensile strength (r = 0.91), point load index (r = 0.87), and Schmidt hammer value (r = 0.90). In some studies, penetration rate was selected as a rock classifier to study other drill variables. Liu and Yin (2001) used penetration rate to monitor drill performance in their investigation of relationships among monitored drill variables in an open-pit iron ore mine. They suggest rate of penetration and specific energy as indicators of drill performance; however, they list many factors affecting these indicators such as rock strength, degree of fracturing, condition of drill and bit, drill variables, and experience of the operator. In summary, while most authors agree that penetration rate can indicate rock strength, many also conclude that geology is not the only influence on rate of penetration. It is only a true indicator of rock strength when all other factors are kept constant - and this is not 24

37 realistic in a production setting at a mine site. With the computational tools available today other methods should be explored. Torque While not as popular as penetration rate, torque is usually found to be reflective of geology as a dependent variable. Scoble et al found that torque increased with rock strength. Their regression analysis showed a strong, positive correlation between applied torque and compressive strength. As previously mentioned, they observed that, in hard rock, changes in properties were even more closely reflected by torque and penetration rate. They concluded that rotary torque patterns indicated hard or soft rock, but torque could also reflect changes in other variables. As mentioned previously, Schunnesson studied the effectiveness of normalized torque, torque variability, normalized penetration rate and penetration rate variability in identifying rock types and properties in underground mines that used percussive drilling. He found that, in simple geologies, these variables could be used to directly classify rock type, while in more complex geologies they could more accurately indicate rock homogeneity and rock hardness. In summary, while useful as a rock property indicator, torque has the same limitations as penetration rate. Thrust As an independent variable, thrust is not usually investigated as a rock type indicator. Most authors immediately dismiss it. However, Scoble et al found that thrust does 25

38 increase with strength. They concluded that thrust decreased in soft rock transitions and increased in harder-stronger transitions. In summary, while thrust is heavily operatordependent, it should be investigated as an indicator of transitions and rock types. Rotary Speed As with thrust, rotary speed is not often studied as a rock type indicator. This was validated by Scoble et al when they found that rotary speed remained constant and did not reflect changes in geology. SFE There have been some conflicting results concerning SFE in previous works. Teale (1965) constructed SFE to relate monitored rotary drill responses to rock properties with one variable. After deriving SFE, he compared minimum SFE to compressive rock strength in data from numerous sources. He concluded that SFE was comparable to compressive rock strength. However, Mellor (1972) found SFE to be proportional to compressive strength with a factor of Rabia (1982) studied SFE and its abilities to predict drill performance, and found that SFE is not an intrinsic property of the rock. He concluded that there were too many other factors affecting SFE, including size of charge, mode of rock breakage and bit type. Scoble et al compared measured compressive rock strength and various specific energy equations (Teale (1965), Rabia (1982), and Bauer and Calder (1966)) and found that the equation developed by Teale performed the best on the small data set they had. They 26

39 believed that this was due to Teale s incorporation of torque. With regressive analysis, Scoble et al identified strong relationships between SFE and measured compressive rock strength. They determined that relative changes in SFE indicated changes in the rock characteristics. As with rate of penetration, Liu and Yin simply accepted SFE as a drill performance indicator without testing it. They do mention rock strength, degree of fracturing, condition of drill and bit, drill settings, and experience of the operator as factors that influence SFE. In summary, caution should be exercised when using SFE to classify rock types. It could be used to indicate changes in rock properties, but may provide inaccurate results if used to determine exact rock strength for rock identification. The relationship between SFE and rock strength is not straightforward: there is evidence that SFE is dependent on the drill and its settings. It is only an indicator of rock strength when it is at a minimum (i.e. at maximum efficiency), which is difficult to determine without testing of the drill and tuning of the drilling settings to be used in that specific rock type. In addition, since SFE is related to rock strength, it would not necessarily distinguish between two rock types of similar strength. Some considerations for drill variable response analysis are listed below: 1. Geology is not the only influence on drill variable response. Other influences are the tendencies of the operator, bit wear, the depth of the hole, and correlations 27

40 among the variables themselves. (Schunnesson, 1997) Schunnesson determined their effect was so significant that he devised a method of normalizing the drill variables in order to remove their influence. 2. There are several stages to the drilling process, such as collaring, normal drilling, breaking, hoisting, etc. Each of these stages is reflected in the monitored variables. However, normal drilling is the only stage that can truly reflect rock properties and only normal drilling data should be included in MWD analysis. (Yue et al, 2003) 3. Care should be taken when selecting a basis of comparison for the MWD analysis results. Geophysical logging of holes is an expensive, time consuming process. Sometimes shortcuts are taken when determining the geology, e.g. by extrapolating between widely spaced core logged holes, which can lead to inaccuracies in the basis for comparison. There is a general agreement that penetration rate and torque best reflect geology, although they are not sufficient as rock classifiers on their own. While thrust is not usually studied, Scoble et al concluded that it is worth investigating. Rotary speed is not indicative of geology. SFE may be reflective of changes in rock strength, although its ability to determine actual rock strength is disputed. Overall, most authors agree that MWD shows promise in the area of geology identification. 28

41 2.3.4 MWD with Neural Networks for Identification of Geology Previous works have determined that geology cannot be identified through one single drilling variable. It is necessary to see how multiple variables interact with each other in different types of geology. Unfortunately it is difficult to fully understand all of the interactions that occur during the drilling process due to its complexity. For this reason pattern recognition techniques have been applied to MWD to improve monitored drill response analysis. As discussed in section 2.2, neural networks are pattern recognition techniques that do not require a prior model, or full understanding, of the process being analyzed. Neural networks have been extensively applied to MWD in coal mines, with the goal of determining roof strength in underground mines or identifying lithology in open-pit mines. Most authors agree that neural networks can successfully be applied to identify geology in coal mines. King et al (1993) analyzed roof bolt drilling data from an underground coal mine in the western states to monitor roof safety. Most of the holes were drilled through sandstone. Unsupervised neural networks were applied as a pattern recognition technique due to the lack of a target training set. Torque, rotation speed, thrust, and penetration rate were the input variables. SFE was used to rank the resulting clusters in order of relative strength. Because of the similarity in rock strengths the clusters were difficult to visualize; however, King et al believed that a larger data set with a greater range in rock strength 29

42 would not have the same problem. While the actual performance of the neural network was not mentioned, they concluded that neural networks were successful in identifying patterns in the roof bolter drill data. Utt (1999) backed these conclusions in his investigation of the ability of neural networks to identify strength in coal mine roof strata. He measured variables on a roof bolt drill, and used SOM to identify various levels of strength in the roof strata. Penetration rate and SFE were the inputs to the SOM and relative strength ratings were the outputs. He was able to construct a lab prototype drill with a monitoring system, which recorded variables, which were then used the SOM for analysis. Based on the output from the SOM, the prototype was able to indicate to the operator the strength of the roof. While Utt also did not mention the accuracy of his prototype, he concluded that this technology should be applicable to all underground mines. LaBelle et al (2000) reached a similar conclusion from their work in underground coal mines. They monitored the magnitudes and standard deviations of thrust, torque, penetration rate, and rotation speed. They used backpropagation neural networks to analyze the monitored roof bolt drill response in a sample concrete block and an underground coal mine. The sample concrete block was composed of five layers of varying strength. Several networks were constructed with various combinations of the monitored variables as inputs. LaBelle et al were able to successfully train their networks to identify the relative strengths of the materials drilled, with the best performing network 30

43 having an average error rate of 4.5%. They concluded that thrust and torque, in that order, were most important for rock classification, and that inclusion of the standard deviations as inputs improved accuracy. Martin (2007) also found that neural networks could identify geology in an open-pit coal mine environment. He applied neural networks to MWD data from a rotary blast-hole drill in an open-pit coal mine. Two measured drill variables, torque and penetration rate, and one variable from an external source, geophysical density, were selected as inputs to his backpropagation neural networks. Part of his thesis explored possible sources of target training sets. He investigated two possibilities: using an expert geologist to label the rock types and training an SOM with the monitored drill variables to cluster the data. Martin concluded that the SOM provided a better target training set, and adding geophysical density improved classification. In summary, neural networks have been proven as rock classifiers in both underground and open-pit coal mining environment. The difference between the research in this thesis work and the previous work reported above is the environment in which the data is gathered. Martin s data was from an open-pit coal mine, with relatively soft rock and a greater range in rock strengths. The data for this thesis is from an open-pit iron ore mine, with hard rock and a smaller range in mechanical properties. The research in this thesis explores and tests previous results and conclusions in a different mining context. 31