MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network

Picking the first arrival times of prestack gathers is called First Arrival Time (FAT) picking, which is an indispensable step in seismic data processing, and is mainly solved manually in the past. With the current increasing density of seismic data collection, the efficiency of manual picking has been unable to meet the actual needs. Therefore, automatic picking methods have been greatly developed in recent decades, especially those based on deep learning. However, few of the current supervised deep learning-based method can avoid the dependence on labeled samples. Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR). In this paper, for hard rock seismic gather data, we propose a Multi-Stage Segmentation Pickup Network (MSSPN), which solves the generalization problem across worksites and the picking problem in the case of low SNR. In MSSPN, there are four sub-models to simulate the manually picking processing, which is assumed to four stages from coarse to fine. Experiments on seven field datasets with different qualities show that our MSSPN outperforms benchmarks by a large margin.Particularly, our method can achieve more than 90\% accurate picking across worksites in the case of medium and high SNRs, and even fine-tuned model can achieve 88\% accurate picking of the dataset with low SNR

Application of seismic monitoring in caving mines

Comprehensive and reliable seismic analysis techniques can aid in achieving successful inference of rockmass behaviour in different stages of the caving process. This case study is based on field data from Telfer sublevel caving mine in Western Australia. A seismic monitoring database was collected during cave progression and breaking into an open pit 550 m above the first caving lift. Five seismic analyses were used for interpreting the seismic events. Interpretation of the seismic data identifies the main effects of the geological features on the rockmass behaviour and the cave evolution. Three spatial zones and four important time periods are defined through seismic data analysis. This thesis also investigates correlations between the seismic event rate, the rate of the seismogenic zone migration, mucking rate, Apparent Stress History, Cumulative Apparent Volume rate and cave behaviour, in order to determine failure mechanisms that control cave evolution at Telfer Gold mine.Master of Applied Science (M.A.Sc.) in Natural Resources Engineerin

Spatial Data Quality in the IoT Era:Management and Exploitation

Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications

Practical Applications of Machine Learning to Underground Rock Engineering

Rock mechanics engineers have increasing access to large quantities of data from underground excavations as sensor technologies are developed, data storage becomes cheaper, and computational speed and power improve. Machine learning has emerged as a viable approach to process data for engineering decision making. This research investigates practical applications of machine learning algorithms (MLAs) to underground rock engineering problems using real datasets from a variety of rock mass deformation contexts. It was found that preserving the format of the original input data as much as possible reduces the introduction of bias during digitalization and results in more interpretable MLAs. A Convolutional Neural Network (CNN) is developed using a dataset from Cigar Lake Mine, Saskatchewan, Canada, to predict the tunnel liner yield class. Several hyperparameters are optimized: the amount of training data, the convolution filter size, and the error weighting scheme. Two CNN architectures are proposed to characterize the rock mass deformation: (i) a Global Balanced model that has a prediction accuracy >65% for all yield classes, and (ii) a Targeted Class 2/3 model that emphasizes the worst case yield and has a recall of >99% for Class 2. The interpretability of the CNN is investigated through three Input Variable Selection (IVS) methods. The three methods are Channel Activation Strength, Input Omission, and Partial Correlation. The latter two are novel methods proposed for CNNs using a spatial and temporal geomechanical dataset. Collectively, the IVS analyses indicate that all the available digitized inputs are needed to produce good CNN performances. A Long-Short Term Memory (LSTM) network is developed using a dataset for Garson Mine, near Sudbury, Ontario, Canada, to predict the stress state in a FLAC3D model. This is a novel method proposed to semi-automate calibration of finite-difference models of high-stress environments. A workflow for optimizing the hyperparameters of the LSTM network is proposed. The performance of the LSTM network predicting the three principal stresses is improved as compared to predicting the six-component stress tensor, with corrected Akaike Information Criterion (AICc) values of -59.62 and -45.50, respectively. General recommendations are made with respect to machine learning algorithm development for practical rock engineering problems, in terms of how to format and pre-process inputs, select architectures, tune hyperparameters, and determine engineering verification metrics. Recommendations are made to demonstrate how algorithms can be rendered interpretable with the application of tools that already exist in the field of machine learning

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Proceedings of the 8th International Conference on Civil Engineering

This open access book is a collection of accepted papers from the 8th International Conference on Civil Engineering (ICCE2021). Researchers and engineers have discussed and presented around three major topics, i.e., construction and structural mechanics, building materials, and transportation and traffic. The content provide new ideas and practical experiences for both scientists and professionals

Proceedings of the 8th International Conference on Civil Engineering

This open access book is a collection of accepted papers from the 8th International Conference on Civil Engineering (ICCE2021). Researchers and engineers have discussed and presented around three major topics, i.e., construction and structural mechanics, building materials, and transportation and traffic. The content provide new ideas and practical experiences for both scientists and professionals

Proceedings of the 2004 Coal Operators\u27 Conference

Proceedings of the 2004 Coal Operators\u27 Conference. All papers in these proceedings are peer reviewed in accordance with The AUSIMM publication standard

Artificial intelligence to detect and forecast earthquakes

Precursors to large earthquakes have been widely but not systematically identified. The ability of deep neural networks to solve complex tasks that involve generalisations makes them highly suited to earthquake and precursor detection. Large moment magnitude (Mw) earthquakes and associated tsunamis can have a huge economic and social impact. Detecting precursors could significantly improve seismic hazard preparedness, particularly if precursors can assist, within a more general probabilistic forecasting framework, in reducing the uncertainty interval on expected earthquakes’ timing, location and Mw. Additionally, artificial intelligence has recently been used to improve the detection and location of smaller earthquakes, assisting in the completion and automation of seismic catalogues. This paper is the first to present a deep learning-based solution for detecting and identifying short-term changes in the raw seismic signal, correlated to earthquake occurrence. Deep neural networks (DNNs) were employed to investigate the background seismic signal prior to 31 Mw >= 6 earthquakes in the Japan region. Instantaneous, precursor-related features (features correlated to the investigated earthquakes) were detected as opposed to predicting future values based on previously observed values in the case of time series forecasting. The network achieved a 98% train accuracy and a 96% test accuracy classifying noise unrelated to Mw >= 6 earthquakes from signal immediately prior to the investigated earthquakes. Additionally, the precursor-related features became increasingly systematic (more frequently detected prior to the investigated earthquakes) with earthquake proximity. Discriminative features appeared most dominant over a frequency range of ~ 0.1-0.9 Hz, coinciding with microseismic noise and recent observations of broadband slow earthquake signal (Masuda et al. 2020). In particular, frequencies of ~ 0.16 and ~ 0.21 Hz provided significant precursor-related information. Deep learning successfully detected features of the seismic data correlated to earthquake occurrence. Developing a better understanding of the origin of the precursor-related features and their reliability is the next step towards establishing an earthquake forecasting system

Fracture characterisation and performance evaluation of corroded RC members by AE-based data analysis

Steel reinforcement corrosion has been regarded as one of the major causes of reinforced concrete (RC) structures failing prematurely, posing a serious structural durability problem worldwide. Detailed assessment of corrosion-induced damage and its effects on RC structures is critical for sustaining structural reliability and safety. This study develops and examines the feasibility of acoustic emission (AE) monitoring and data analysis methodologies to characterise corrosion-induced damage in RC members, followed by an evaluation of the effect of corrosion on load behaviour. Experimental investigations were conducted on a series of specimens of different configurations, namely concrete cubes with steel bars for pull-out tests and RC beams of different dimensions to be subjected to static and cyclic loading regimes. Focusing on developing evaluation methods based on AE monitoring and data analysis, a summary of work completed, and the associated findings are given as follows. Characterisation of the concrete cracking using parametric and waveform analysis was conducted to investigate the effect of corrosion on steel-concrete bond behaviour in the pull-out tests of concrete cubes. It was found that a small amount of corrosion (approximately 6%) could slightly increase the bond strength as a result of the rust expansion and reactionary confinement of concrete. Corrosion was also found to be able to mitigate the damage caused by cyclic loading. AE signal analysis indicates that the concrete cracking mode during the steel-concrete de-bonding process has changed as a result of steel corrosion. Characterisation of load behaviour and failure mode of corroded RC beams was conducted by flexural load tests aided by AE monitoring and digital image correlation (DIC). The DIC strain mapping results and AE signal features revealed that corrosion has an influence on the concrete cracking mechanism of the beam specimens. Corrosion has also altered the failure mode of a shear-critical beam specimen series to flexure owing to the change of steel-concrete bond behaviour. Numerical simulation of AE wave front propagation in RC media and tomographic evaluation of internal damage was implemented on one group of RC beam specimens tested in this study. The numerical model of the specimens was discretised using finite-difference grid meshing, and the different acoustic properties of steel and concrete were defined. On this basis, simulation of AE wave front propagation considering concrete cover cracking and steel rust layer formation was carried out using the fast-marching method. The effect of corrosion-induced damage on the AE rays was studied by examining non-linear ray tracing in the simulation. A tomographic reconstruction approach that solved by the quasi-Newton method provided a potential way to quantitatively evaluate the internal damage of RC beams using AE monitoring data. A novel method was developed for assessing the corrosion level in RC beams using a data-driven approach. Normalization of AE data was applied using principal component analysis to minimise variations in AE signal features caused by differences in the geometrical and material properties of RC beams as well as in the AE monitoring instrumentation setup. The machine learning models, including k-nearest neighbours (KNN) and support vector machines (SVM), were trained using the normalised AE features. The trained KNN models were found effective at predicting the corrosion level in RC beams using the secondary AE signals as input, which could be acquired from the cyclic loading of beams. Key words: Steel Corrosion, Concrete cracking, Steel-Concrete Bond, Reinforced Concrete Beam, Load Behaviour, Acoustic Emission, Digital Image Correlation, Tomographic Reconstruction, Data-driven