Automatic Classification of Microseismic Records in Underground Mining: A Deep Learning Approach

Peng, Pingan; Wang, Liguan; Jiang, Yuanjian

doi:10.1109/access.2020.2967121

Cited by 24 publications

(18 citation statements)

References 33 publications

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“…Table 3 gives an overview of the 21 features employed most frequently in the literature for each frame used in this study. It is worth mentioning that these features are selected by the genetic algorithm (GA)-optimized correlation-based feature selection (CFS) method, for more detail implementation of feature selection, please see the reference 35 , 36 . Zero-crossing rates are used to determine whether the microseismic record is present in a frame 37 .…”

Section: Methodsmentioning

confidence: 99%

Microseismic records classification using capsule network with limited training samples in underground mining

Peng

Wang

Jiang

2020

Sci Rep

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The identification of suspicious microseismic events is the first crucial step in microseismic data processing. Existing automatic classification methods are based on the training of a large data set, which is challenging to apply in mines without a long-term manual data processing. In this paper, we present a method to automatically classify microseismic records with limited samples in underground mines based on capsule networks (CapsNet). We divide each microseismic record into 33 frames, then extract 21 commonly used features in time and frequency from each frame. Consequently, a 21 × 33 feature matrix is utilized as the input of CapsNet. On this basis, we use different sizes of training sets to train the classification models separately. The trained model is tested using the same test set containing 3,200 microseismic records and compared to convolutional neural networks (CNN) and traditional machine learning methods. Results show that the accuracy of our proposed method is 99.2% with limited training samples. It is superior to CNN and traditional machine learning methods in terms of Accuracy, Precision, Recall, F1-Measure, and reliability. Underground engineering causes disturbances in the stress state of the rock mass, leading to a large number of microseismic events 1. By post-processing these records (e.g., P-wave arrival picking 2 , event location 3 , and source parameter calculation 4-6), the mechanical state of the corresponding rock mass can be adequately reflected, which is beneficial especially for disaster early warning in underground mining 7-9. However, in the underground mining process, the microseismic monitoring system often receives interference from blasting operations, ore extraction, mechanical operations, high voltage cables, and magnetic fields 10. Therefore, quickly and accurately identifying microseismic records from a large number of suspicious records is a crucial task. Currently, the classification of suspicious microseismic records depends on the visual scanning of waveforms by experienced analysts 11. However, manual classification of microseismic records is a time-consuming, tedious task that is easy to bring into subjective opinions. For these reasons, automatic classification of microseismic records is urgently needed. Throughout the years, many automatic classification methods have been proposed to address the abovementioned problems in seismic and microseismic fields. Scarpetta et al. 12 established a specialized neural discrimination method for low magnitude seismic events, quarry blasts, underwater explosions, and thunder sources at Mt. Vesuvius Volcano, Italy. Langer 13 , Esposito 14 and Curilem 15 used the machine learning to classify seismic records at the Soufriere Hills volcano (Montserrat), Stromboli island (southern Italy) and the Villarrica volcano (Chile), respectively. Malovichko 16 utilized a set of seismic characteristics and the multivariate maximum-likelihood Gaussian classifier, to quantify a probability that a particular event belongs to a population of blas...

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Section: Methodsmentioning

confidence: 99%

Microseismic records classification using capsule network with limited training samples in underground mining

Peng

Wang

Jiang

2020

Sci Rep

Self Cite

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“…Our mining-induced microseismic data came from the Huangtupo Copper and Zinc Mine, located in Hami City, China. For the specific conditions of this mine refer to the existing literature [39][40][41]. A mining-induced microseismic monitoring system was installed to monitor the safety of goaves in the mine.…”

Section: Datasetmentioning

confidence: 99%

A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing

Wang

Peng

2022

Applied Sciences

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Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance r is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of r are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value μ and variance value σ2 of that set, we define the decomposition stability w as μ/σ2. Then, the wavelet that maximizes w for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals.

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“…In order to present data in an interpretive way, one or the other approach to feature selection are usually implemented. For example, in the work [33] the authors tried to apply 35 parameters such as signal energy E or the entropy EE of the energy of each frame E n to a signal S:…”

Section: Feature Extractionmentioning

confidence: 99%

Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural Networks

Nedorubova,

Kadyrova,

Khlyupin

2021

Preprint

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Automatic Classification of Microseismic Records in Underground Mining: A Deep Learning Approach

Cited by 24 publications

References 33 publications

Microseismic records classification using capsule network with limited training samples in underground mining

Microseismic records classification using capsule network with limited training samples in underground mining

A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing

Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural Networks

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