Key Takeaways & Executive Findings
- •• WM-ResNet50, integrating data enhancement, deep CNN, and CBAM, achieves 94.38% average recognition accuracy for grouting-induced microseismic signals, outperforming baseline models. • The hybrid model effectively classifies microseismic signals in deep rock layers, enabling accurate prediction of slurry diffusion ranges during high-pressure grouting. • Field and laboratory validations confirm that WM-ResNet50-classified MS sources match grouting records, demonstrating its practical utility in mining safety. • The study proposes a solution to address insufficient grouting diffusion under long-borehole conditions, validated via in-situ coring and XRD analysis.
Abstract
Microseismic (MS) monitoring is an effective technique to detect mining-induced rock fractures. However, recognizing grouting-induced signals is challenging due to complex geological conditions in deep rock plates. Therefore, a hybrid model (WM-ResNet50) integrating data enhancement, a deep convolutional neural network (CNN), and convolutional block attention modules (CBAM) was proposed. Firstly, an MS system was established at the Xieqiao coal mine in Anhui Province, China. MS waveforms and injection parameters were acquired during grouting. Secondly, signals were categorized based on time–frequency characteristics to build a dataset, which was divided into training, validation, and test sets at a ratio of 4:1:1. Subsequently, the performance of WM-ResNet50 was evaluated based on indices such as individual precision, total accuracy, recall, and loss function. The results indicated that WM-ResNet50 achieved an average recognition accuracy of 94.38%, surpassing that of a simple CNN (90.04%), ResNet18 (91.72%), and ResNet50 (92.48%). Finally, WM-ResNet50 was applied to monitor the whole process at laboratory tests and field cases. Both results affirmed the feasibility and effectiveness of MS inversion in predicting actual slurry diffusion ranges within deep rock layers. By comparison, it was revealed that the MS sources classified by WM-ResNet50 matched grouting records well. A solution to address insufficient diffusion under long-borehole grouting has been proposed. WM-ResNet50's accuracy was validated through in-situ coring and XRD analysis for cement-based hydration products. This study provides a beneficial reference for similar rock signal processing and in-field grouting practices.
1. Introduction
Microseismic (MS) monitoring is an effective remote-sensing technique used to detect physical rock fracturing that is strong enough to instigate vibrations [1]. It has been used in multiple fields, including oil and gas exploration [2], early warning for rock bursts [1], and underground projects [3,4]. For coal mines, grouting has been adopted to reinforce rock masses before mining in previous cases [5,6]. In theory, the flow of slurry within rocks will generate fractures and elastic waves, which will be recorded by geophones. By further signal processing, grouting-induced damages will be perceived (e.g., time, location, and magnitude) [7].
However, there are few studies applying the MS technique to monitor the grouting process [8]. The reason is that the signal-to-noise ratio of grouting signals is low for complex conditions and disturbances. Identifying grouting-induced waveforms is the first step for further application. How to estimate slurry diffusion scopes by MS parameters is the current challenge for guaranteeing mining safety.
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Yongshu Zhang, Lianchong Li, Wenqiang Mu, Jian Chen, Peng Chen (2025). An attention module integrated hybrid model for recognizing microseismic signals induced by high-pressure grouting in deep rock layers. SinoTechIntel Verified Research. https://doi.org/10.1016/j.ijmst.2025.12.008
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a hybrid model (WM-ResNet50) that integrates data enhancement, deep CNN, and CBAM attention modules to accurately recognize microseismic signals induced by high-pressure grouting in deep rock layers, achieving 94.38% accuracy and enabling reliable prediction of slurry diffusion ranges.
How does WM-ResNet50 compare to other models?
WM-ResNet50 outperforms simple CNN (90.04%), ResNet18 (91.72%), and ResNet50 (92.48%) in average recognition accuracy, demonstrating the effectiveness of integrating attention mechanisms.
What data was used to train and test the model?
Microseismic waveforms and injection parameters were acquired from the Xieqiao coal mine in Anhui Province, China. Signals were categorized based on time-frequency characteristics and split into training, validation, and test sets in a 4:1:1 ratio.
How was the model validated in real-world scenarios?
The model was applied to laboratory tests and field cases, where MS sources classified by WM-ResNet50 matched grouting records. In-situ coring and XRD analysis confirmed the accuracy of the model in predicting slurry diffusion.
What practical implications does this study have?
The study provides a beneficial reference for similar rock signal processing and in-field grouting practices, offering a solution to address insufficient diffusion under long-borehole grouting and enhancing mining safety.
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