Intelligent Location of Microseismic Events Based on a Fully Convolutional Neural Network (FCNN)

Ma, Keping; Sun, Xiaotong; Zhang, Zhenghu; Hu, Jing; Wang, Zuorong

doi:10.1007/s00603-022-02911-x

Cited by 25 publications

(9 citation statements)

References 31 publications

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“…Microseismic monitoring technology (MMT) has found extensive applications in underground engineering for disasters and safety monitoring [1]. Specifically, it has been utilized for location monitoring [2], [3], as well as forecasting and providing early warning systems for rock bursts [4], and mine earthquake disasters during mining operations [5], [6]. The basic principle involves identifying microseismic events by analyzing prominent features within the monitoring data [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-channel microseismic signals classification with convolutional neural networks

Shu,

Dawod,

Tepsan

et al. 2024

IJ-AI

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Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.

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Section: Introductionmentioning

confidence: 99%

Multi-channel microseismic signals classification with convolutional neural networks

Shu,

Dawod,

Tepsan

et al. 2024

IJ-AI

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“…It involves the use of acoustic-electric sensors to monitor the generation of elastic waves during the rock mass failure process, thereby obtaining spatiotemporal strength information related to microseismic events and facilitating the prediction of mining-induced dynamic disasters [5][6][7][8]. Before predicting mining-induced dynamic disasters, a series of preprocessing steps are required for microseismic signals: microseismic signal classification [9][10][11][12][13], microseismic signal denoising [14,15], initial arrival time picking [16,17], and seismic source localization [18][19][20]. Microseismic signal classification represents the first step in the preprocessing of microseismic signals and is a crucial component to ensure the effectiveness of subsequent procedures.…”

Section: Introductionmentioning

confidence: 99%

Classification of Microseismic Signals Using Machine Learning

Chen,

Cui,

et al. 2024

Processes

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The classification of microseismic signals represents a fundamental preprocessing step in microseismic monitoring and early warning. A microseismic signal source rock classification method based on a convolutional neural network is proposed. First, the characteristic parameters of the microseismic signals are extracted, and a convolutional neural network is constructed for the analysis of these parameters; then, the mapping relationship model between the characteristic parameters of the microseismic signals and the rock class is established. The feasibility of the proposed method in differentiating acoustic emission signals under different load conditions is verified by using acoustic emission data from laboratory uniaxial compression tests, Brazilian splitting tests, and shear tests. In the three distinct laboratory experiments, the proposed method achieved a source rock classification accuracy of greater than 90% for acoustic emission signals. The proposed and verified method provides a new basis for the preprocessing of microseismic signals.

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“…Discerning ongoing processes of rock slope deformation that may lead to instability covers essential miscellaneous aspects of engineering geology and geomechanics [10][11][12]. The management of and substantial information on slope failure-associated risks are integral to having an adequate understanding of the lithostructural predisposition, the driving forces, and the different mechanisms and environmental conditions in the monitored area [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method

Tao,

Zhang,

2024

Water

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In the field of open-pit geological risk management, landslide failure time prediction is one of the important topics. Based on the analysis of displacement monitoring data, the inverse velocity method (INV) has become an effective method to solve this issue. To improve the reliability of landslide prediction, four filters were used to test the velocity time series, and the effect of landslide failure time prediction was compared and analyzed. The results show that the sliding process of landslide can be divided into three stages based on the INV: the initial attenuation stage (regressive stage), the second attenuation stage (progressive stage), and the linear reduction stage (autoregressive stage). The accuracy of the INV is closely related to the measured noise of the monitoring equipment and the natural noise of the environment, which will affect the identification of different deformation stages. Compared with the raw data and the exponential smoothing filter (ESF) models, the fitting effect of the short-term smoothing filter (SSF) and long-term smoothing filter (LSF) in the linear autoregressive stage is better. A stratified prediction method combining SSF and LSF is proposed. The prediction method is divided into two levels, and the application of this method is given.

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Intelligent Location of Microseismic Events Based on a Fully Convolutional Neural Network (FCNN)

Cited by 25 publications

References 31 publications

Multi-channel microseismic signals classification with convolutional neural networks

Multi-channel microseismic signals classification with convolutional neural networks

Classification of Microseismic Signals Using Machine Learning

Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method

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