Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform

Dong, Longjun; Tang, Zheng; Li, Xibing; Chen, Yongchao; Xue, Jinchun

doi:10.1007/s11771-020-4530-8

Cited by 75 publications

(40 citation statements)

References 39 publications

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“…Similarly, machine learning could be applied to induced seismicity, mine blasts, or volcanic signals. While some studies in this direction exist (Chai et al., 2020; Dong et al., 2020; Lapins et al., 2021), there is not yet a comprehensive study. To facilitate such a study, comprehensive benchmark datasets with rich metadata need to be assembled.…”

Section: Open Questionsmentioning

confidence: 99%

Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers

et al. 2022

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Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches, achieving human‐like performance under certain circumstances. However, as studies differ in the datasets and evaluation tasks, it is unclear how the different approaches compare to each other. Furthermore, there are no systematic studies about model performance in cross‐domain scenarios, that is, when applied to data with different characteristics. Here, we address these questions by conducting a large‐scale benchmark. We compare six previously published deep learning models on eight data sets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer‐Kradolfer picker. Overall, we observe the best performance for EQTransformer, GPD and PhaseNet, with a small advantage for EQTransformer on teleseismic data. Furthermore, we conduct a cross‐domain study, analyzing model performance on data sets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but models trained on regional data do not transfer well to teleseismic data. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily evaluate new models or performance on new data sets. Furthermore, we make all trained models available through the SeisBench framework, giving end‐users an easy way to apply these models.

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Section: Open Questionsmentioning

confidence: 99%

Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers

et al. 2022

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“…In recent years, machine learning and convolutional neural networks have shown a strong capability for feature extraction and target detection, and they have been used in structural health monitoring (SHM). For example, Dong [ 16 ] established a CNN model to identify microseismic events and blasts accurately. Yu [ 17 ] put forward a DCNN-based method to localize damages to smart building structures with high accuracy on raw noisy signals.…”

Section: Introductionmentioning

confidence: 99%

Crack Length Measurement Using Convolutional Neural Networks and Image Processing

Yuan

Zhang

et al. 2021

Sensors

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Fatigue failure is a significant problem in the structural safety of engineering structures. Human inspection is the most widely used approach for fatigue failure detection, which is time consuming and subjective. Traditional vision-based methods are insufficient in distinguishing cracks from noises and detecting crack tips. In this paper, a new framework based on convolutional neural networks (CNN) and digital image processing is proposed to monitor crack propagation length. Convolutional neural networks were first applied to robustly detect the location of cracks with the interference of scratch and edges. Then, a crack tip-detection algorithm was established to accurately locate the crack tip and was used to calculate the length of the crack. The effectiveness and precision of the proposed approach were validated through conducting fatigue experiments. The results demonstrated that the proposed approach could robustly identify a fatigue crack surrounded by crack-like noises and locate the crack tip accurately. Furthermore, crack length could be measured with submillimeter accuracy.

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“…At present, due to the excellent feature extraction ability of deep learning technology than traditional methods, it has achieved great success in computer vision, machine translation, signal processing, and other fields (Lv et al 2019;Singh et al 2021). However, the research in mine MS monitoring is still in its infancy (Dong et al 2020). At present, the feature learning ability of the convolutional neural network (CNN) has been recognized and rapidly developed with the advances made possible by modern computers and big-data techniques (Gu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

A hybrid recognition model of microseismic signals for underground mining based on CNN and LSTM networks

Zhao

Yang

et al. 2021

Geomatics, Natural Hazards and Risk

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Microseismic (MS) monitoring technology has been widely used to monitor ground pressure disasters. However, the underground mining environment is complex and contains many types of noise sources. Furthermore, the traditional recognition method entails a complex process with low recognition accuracy for MS signals, so it is difficult to serve for the safe production of mines. Therefore, this study established a hybrid model combining the singular spectrum analysis (SSA) method, convolutional neural networks (CNN), and long short-term memory networks (LSTM). First, the principal components of monitoring signals were extracted with the SSA method, and then spatial and temporal features of monitoring signals were separately extracted with the CNN and LSTM. Based on actual field data collected from Xiadian Gold Mine, the hybrid model was compared with the CNN, LSTM, and backpropagation networks (BP), as well as commonly used recognition methods including the support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and linear discriminant analysis (LDA). The results show that the proposed hybrid model can accurately extract data features of monitoring signals and further improve MS signals' recognition performance. Furthermore, the recognition accuracy of mechanical signals in monitoring signals is particularly increased using the hybrid model, which avoids confusion with MS signals.

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Discrimination of mining microseismic events and blasts using convolutional neural networks and original waveform

Cited by 75 publications

References 39 publications

Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers

Which Picker Fits My Data? A Quantitative Evaluation of Deep Learning Based Seismic Pickers

Crack Length Measurement Using Convolutional Neural Networks and Image Processing

A hybrid recognition model of microseismic signals for underground mining based on CNN and LSTM networks

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