A new signal processing method for acoustic emission/microseismic data analysis

The acoustic emission/microseismic technique (AE/MS) has emerged as one of the most important techniques in recent decades and has found wide applications in different fields. Extraction of seismic event with precise timing is the first step and also the foundation for processing AE/MS signals. However, this process remains a challenging task for most AE/MS applications. The process has generally been performed by human analysts. However, manual processing is time consuming and subjective. These challenges continue to provide motivation for the search for new and innovative ways to improve the signal processing needs of the AE/MS technique. This research has developed a highly efficient method to resolve the problems of background noise and outburst activities characteristic of AE/MS data to enhance the picking of P-phase onset time. The method is a hybrid technique, comprising the characteristic function (CF), high order statistics, stationary discrete wavelet transform (SDWT), and a phase association theory. The performance of the algorithm has been evaluated with data from a coal mine and a 3-D concrete pile laboratory experiment. The accuracy of picking was found to be highly dependent on the choice of wavelet function, the decomposition scale, CF, and window size. The performance of the algorithm has been compared with that of a human expert and the following pickers: the short-term average to long-term average (STA/LTA), the Baer and Kradolfer, the modified energy ratio, and the short-term to long-term kurtosis. The results show that the proposed method has better picking accuracy (84% to 78% based on data from a coal mine) than the STA/LTA. The introduction of the phase association theory and the SDWT method in this research provided a novelty, which has not been seen in any of the previous algorithms --Abstract, page iii

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Advanced Techniques and Efficiency Assessment of Mechanical Processing

Mechanical processing is just one step in the value chain of metal production, but to some exten,t it determines an effectiveness of separation through suitable preparation of the raw material for beneficiation processes through production of required particle sze composition and useful mineral liberation. The issue is mostly related to techniques of comminution and size classification, but it also concerns methods of gravity separation, as well as modeling and optimization. Technological and economic assessment supplements the issue

Advanced satellite radar interferometry for small-scale surface deformation detection

Synthetic aperture radar interferometry (InSAR) is a technique that enables generation of Digital Elevation Models (DEMs) and detection of surface motion at the centimetre level using radar signals transmitted from a satellite or an aeroplane. Deformation observations can be performed due to the fact that surface motion, caused by natural and human activities, generates a local phase shift in the resultant interferogram. The magnitude of surface deformation can be estimated directly as a fraction of the wavelength of the transmitted signal. Moreover, differential InSAR (DInSAR) eliminates the phase signal caused by relief to yield a differential interferogram in which the signature of surface deformation can be seen. Although InSAR applications are well established, the improvement of the interferometry technique and the quality of its products is highly desirable to further enhance its capabilities. The application of InSAR encounters problems due to noise in the interferometric phase measurement, caused by a number of decorrelation factors. In addition, the interferogram contains biases owing to satellite orbit errors and atmospheric heterogeneity These factors dramatically reduce the stlectiveness of radar interferometry in many applications, and, in particular, compromise detection and analysis of small-scale spatial deformations. The research presented in this thesis aim to apply radar interferometry processing to detect small-scale surface deformations, improve the quality of the interferometry products, determine the minimum and maximum detectable deformation gradient and enhance the analysis of the interferometric phase image. The quality of DEM and displacement maps can be improved by various methods at different processing levels. One of the methods is filtering of the interferometric phase.However, while filtering reduces noise in the interferogram, it does not necessarily enhance or recover the signal. Furthermore, the impact of the filter can significantly change the structure of the interferogram. A new adaptive radar interferogram filter has been developed and is presented herein. The filter is based on a modification to the Goldstein radar interferogram filter making the filter parameter dependent on coherence so that incoherent areas are filtered more than coherent areas. This modification minimises the loss of signal while still reducing the level of noise. A methodology leading to the creation of a functional model for determining minimum and maximum detectable deformation gradient, in terms of the coherence value, has been developed. The sets of representative deformation models have been simulated and the associated phase from these models has been introduced to real SAR data acquired by ERS-1/2 satellites. A number of cases of surface motion with varying magnitudes and spatial extent have been simulated. In each case, the resultant surface deformation has been compared with the 'true' surface deformation as defined by the deformation model. Based on those observations, the functional model has been developed. Finally, the extended analysis of the interferometric phase image using a wavelet approach is presented. The ability of a continuous wavelet transform to reveal the content of the wrapped phase interferogram, such as (i) discontinuities, (ii) extent of the deformation signal, and (iii) the magnitude of the deformation signal is examined. The results presented represent a preliminary study revealing the wavelet method as a promising technique for interferometric phase image analysis

Review of machine learning and deep learning application in mine microseismic event classification

Purpose. To put forward the concept of machine learning and deep learning approach in Mining Engineering in order to get high accuracy in separating mine microseismic (MS) event from non-useful events such as noise events blasting events and others. Methods. Traditionally applied methods are described and their low impact on classifying MS events is discussed. General historical description of machine learning and deep learning methods is shortly elaborated and different approaches conducted using these methods for classifying MS events are analysed. Findings. Acquired MS data from rock fracturing process recorded by sensors are inaccurate due to complex mining environment. They always need preprocessing in order to classify actual seismic events. Traditional detecting and classifying methods do not always yield precise results, which is especially disappointing when different events have a similar nature. The breakthrough of machine learning and deep learning methods made it possible to classify various MS events with higher precision compared to the traditional one. This paper introduces a state-of-the-art review of the application of machine learning and deep learning in identifying mine MS events. Originality. Previously adopted methods are discussed in short, and a brief historical outline of Machine learning and deep learning development is presented. The recent advancement in discriminating MS events from other events is discussed in the context of these mechanisms, and finally conclusions and suggestions related to the relevant field are drawn. Practical implications. By means of machin learning and deep learning technology mine microseismic events can be identified accurately which allows to determine the source location so as to prevent rock burst.Мета. Аналіз й узагальнення технологій машинного і глибокого навчання в гірничодобувній промисловості для високоточної ідентифікації гірських мікросейсмічних (МС) подій на відміну від таких незначних подій як шум, вибух та інші. Методика. Описано традиційні методи класифікації МС подій і показана їх недостатня ефективність. Надана коротка історична довідка про розвиток методів машинного та глибокого навчання, розглянуті різні підходи до використання цих методів при класифікації МС подій. Результати. У статті наведено огляд новітніх способів застосування машинного та глибокого навчання для виявлення шахтних МС подій. Представлені сучасні досягнення в галузі ідентифікації МС подій серед подій іншого роду, зроблені остаточні висновки і пропозиції щодо розглянутих проблем. Відзначено, що відомі дані про МС події, пов’язані з процесом руйнування гірських порід, отримані за допомогою датчиків, не є достатньо точними у зв’язку з комплексним впливом гірського середовища і потребують попередньої обробки перш, ніж використовувати їх для класифікації реальних МС подій. Встановлено, що традиційні способи виявлення та класифікації МС подій не завжди дозволяють отримати точні результати, що особливо важливо, коли події однієї природи мають різні прояви. Розроблено класифікацію різних МС подій з більш високою точністю у порівнянні з існуючими методиками. Наукова новизна. Сформована концепція машинного і глибокого навчання в гірничодобувній промисловості, що дозволяє високоточно ідентифікувати гірські удари в шахтах на відміну від інших видів геодинамічних явищ. Практична значимість. Технології машинного і глибокого навчання дозволяють точно ідентифікувати шахтні МС події і визначити місце розташування їх джерела, що дозволяє запобігти гірського удару та підвищити безпеку ведення гірничих робіт.Цель. Анализ и обобщение технологий машинного и глубокого обучения в горнодобывающей промышленности для высокоточной идентификации горных микросейсмических (МС) событий в отличие от таких незначительных событий как шум, взрыв и другие. Методика. Описаны традиционные методы классификации МС событий и показана их недостаточная эффективность. Дана краткая историческая справка о развитии методов машинного и глубокого обучения, рассмотрены различные подходы к использованию этих методов при классификации МС событий. Результаты. В статье приведен обзор новейших способов применения машинного и глубокого обучения для обнаружения шахтных МС событий. Представлены современные достижения в области идентификации МС событий среди событий другого рода, сделаны окончательные выводы и предложения в отношении рассматриваемых проблем. Отмечено, что известные данные о МС событиях, связанных с процессом разрушения горных пород, полученные с помощью датчиков, не являются достаточно точными из-за комплексного влияния горной среды и нуждаются в предварительной обработке прежде, чем использовать их для классификации реальных МС событий. Установлено, что традиционные способы обнаружения и классификации МС событий не всегда позволяют получить точные результаты, что особенно важно, когда события одной природы имеют различные проявления. Разработана классификация различных МС событий с более высокой точностью по сравнению с существующими методиками. Научная новизна. Сформирована концепция машинного и глубокого обучения в горнодобывающей промышленности, позволяющая высокоточно идентифицировать горные удары в шахтах в отличии от других видов геодинамических явлений. Практическая значимость. Технологии машинного и глубокого обучения позволяют точно идентифицировать шахтные МС события и определить месторасположение их источника, что позволяет предотвратить горный удар и повысить безопасность ведения горных работ.This work has been performed under the acknowledged support of the research team of Mining System Engineering and National Natural Science Foundation of China, working in big data oriented safety hazard identification and accident evolution mechanism of metal underground mines, 52074022

A machine learning approach for the detection of supporting rock bolts from laser scan data in an underground mine

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordRock bolts are a crucial part of underground infrastructure support; however, current methods to locate and record their positions are manual, time consuming and generally incomplete. This paper describes an effective method to automatically locate supporting rock bolts from a 3D laser scanned point cloud. The proposed method utilises a machine learning classifier combined with point descriptors based on neighbourhood properties to classify all data points as either ‘bolt’ or ‘not-bolt’ before using the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to divide the results into candidate bolt objects. The centroids of these objects are then computed and output as simple georeferenced 3D coordinates to be used by surveyors, mine managers and automated machines. Two classifiers were tested, a random forest and a shallow neural network, with the neural network providing the more accurate results. Alongside the different classifiers, different input feature types were also examined, including the eigenvalue based geometric features popular in the remote sensing community and the point histogram based features more common in the mobile robotics community. It was found that a combination of both feature sets provided the strongest results. The obtained precision and recall scores were 0.59 and 0.70 for the individual laser points and 0.93 and 0.86 for the bolt objects. This demonstrates that the model is robust to noise and misclassifications, as the bolt is still detected even if edge points are misclassified, provided that there are enough correct points to form a cluster. In some cases, the model can detect bolts which are not visible to the human interpreter.University of Exete

Locating Anchor Drilling Holes Based on Binocular Vision in Coal Mine Roadways

The implementation of roof bolt support within a coal mine roadway has the capacity to bolster the stability of the encompassing rock strata and thereby mitigate the potential for accidents. To enhance the automation of support operations, this paper introduces a binocular vision positioning method for drilling holes, which relies on the adaptive adjustment of parameters. Through the establishment of a predictive model, the correlation between the radius of the target circular hole in the image and the shooting distance is ascertained. Based on the structural model of the anchor drilling robot and the related sensing data, the shooting distance range is defined. Exploiting the geometric constraints inherent to adjacent anchor holes, the precise identification of anchor holes is detected by a Hough transformer with an adaptive parameter-adjusted method. On this basis, the matching of the anchor hole contour is realized by using linear slope and geometric constraints, and the spatial coordinates of the anchor hole center in the camera coordinate system are determined based on the binocular vision positioning principle. The outcomes of the experiments reveal that the method attains a po-sitioning accuracy of 95.2%, with an absolute error of around 1.52 mm. When compared with manual operation, this technique distinctly enhances drilling accuracy and augments support efficiency

Lifting load monitoring of mine hoist through vibration signal analysis with variational mode decomposition

Mine hoists play a crucial role in vertical-shaft transportation, and one of the main causes of their faults is abnormal lifting load. However, direct measurement of the load value is difficult. Further, the original structure must be destroyed for sensor installation. To facilitate efficient and accurate monitoring of the lifting load of mine hoist, this paper presents a novel condition-monitoring method based on variational mode decomposition (VMD) and support vector machine (SVM) through vibration signal analysis. First, traditional empirical mode decomposition (EMD) is used to analyze the vibration signal collected by an acceleration sensor, and the number of obtained intrinsic mode functions (IMFs) is employed to set the VMD mode number. Second, the obtained vibration signal is processed by the parameterized VMD, and the useful IMFs of VMD are selected through correlation analysis for feature extraction. Third, the obtained features are used to train an SVM model, and the trained SVM is used to monitor the mine-hoist lifting load. In this study, experiments on an operated mine hoist are also conducted to verify the reliability and validity of the proposed method. The experimental results show that the proposed method can accurately identify the considered lifting load conditions

Energy-Efficiency of Conveyor Belts in Raw Materials Industry

This book focuses on research related to the energy efficiency of conveyor transportation. The solutions presented in the Special Issue have an impact on optimizing, and thus reducing, the costs of energy consumption by belt conveyors. This is due, inter alia, to the use of better materials for conveyor belts, which reduce its rolling resistance and noise, and improve its ability to adsorb the impact energy from the material falling on the belt. The use of mobile robots designed to detect defects in the conveyor's components makes the conveyor operation safer, and means that the conveyor works for longer and there are no unplanned stops due to damage