Deep underground, thousands of meters below the surface, every minor rock fracture is like a faint "whisper" from the Earth, containing precursor information of rock mass instability and rockburst. However, amidst the overlapping layers of mining machinery noise, blasting vibrations, and electromagnetic interference, extracting these critical signals is akin to "finding a needle in a haystack." Addressing the challenge of precise identification in mine rockburst monitoring, several recent global cutting-edge studies have leveraged deep learning methods to breakthroughly enhance the sensitivity and accuracy of the "sensory nerves" of smart mines, opening a new path for accurate early warning of mine dynamic disasters.

Deep Learning "Casting a Wide Net": Collective Breakthroughs in Intelligent Microseismic Signal Recognition

In recent years, as mines advance toward deeper extraction, rockburst has become the "number one killer" threatening the safety of underground personnel and production. Microseismic monitoring technology is a core method for capturing the dynamics of rock mass fracturing, but the massive data it generates is filled with numerous blasting, mechanical, and noise signals. The waveform characteristics of these signals are unstable, and weak events are often submerged in the complex underground noise background.

Traditional machine learning algorithms rely on manual feature engineering and suffer from insufficient classification accuracy for non-stationary microseismic signals. Recently, multiple research teams have focused on deep learning to fundamentally solve this international challenge. In the detection and arrival time picking of mine microseismic events, the PhaseNet model based on a deep convolutional neural network achieved an accuracy exceeding 0.9 for P-wave and S-wave arrival time picking on synthetic data, demonstrating its capability to locate low-magnitude events in high-noise environments. The dynamic kernel attention-enhanced CNN-CapsNet model proposed by Wang Kaikai's team at Curtin University in Australia achieved high-precision classification of single-channel and multi-channel microseismic signals. Its multi-channel model attained an accuracy of 90.08% in background noise, with a uniformity index >0.96, proving the model's effectiveness in perceiving the spatial evolution characteristics of microseismic events; their work was published in the first-tier journal International Journal of Rock Mechanics and Mining Sciences (2026). Scholars from East China University of Technology and other institutions proposed a physics-constrained time-frequency synergistic Transformer (PTFST-Former) to capture local mutations and long-term dependencies in microseismic time series. Under small-sample conditions, it effectively prevents overfitting using physical prior constraints, significantly improving the recognition accuracy of rock fracture signals.

In the safety monitoring of grouting engineering, a deep learning hybrid model integrating an attention module proposed by Zhang Y et al. can accurately identify microseismic signals induced by high-pressure grouting in deep rock layers, providing intelligent data support for real-time judgment of grout diffusion range and mitigation of engineering risks. Additionally, for microseismic monitoring of the formation process of water-conducting fracture zones, a study by Tang Zhi's team from Liaoning Technical University, published in the International Journal of Rock Mechanics and Mining Sciences, proposed a deep learning model combining multi-head probabilistic sparse self-attention and causal dilated convolution. This model effectively captures the spatiotemporal evolution patterns of water-conducting fracture development, achieving recognition accuracies of 96.0% and 93.8% on monitoring datasets of water-conducting fracture zones, providing key technical support for roof water hazard prevention in shallow coal seams (Applied Sciences, 2026).

Trillions of Operations Forge "Sharp Eyes": Dual Validation of Recognition Efficiency and Accuracy

Compared to traditional models, novel deep convolutional neural networks (CNNs) not only automatically extract high-dimensional hidden features in the time-frequency domain of signals by training millions or even hundreds of millions of parameters but also achieve a paradigm shift from "feature engineering" to "end-to-end" learning.

A study by Peng Fuyu et al. from Hunan University of Science and Technology, published in the journal Applied Sciences, proposed an automatic recognition method for mine microseismic waveform signals based on the VGG16 convolutional neural network architecture. By integrating time-frequency analysis and image segmentation techniques, the team transformed original one-dimensional microseismic waveform signals into two-dimensional color-enhanced spectrograms containing time-frequency information. They leveraged VGG16's powerful multi-level convolutional feature extraction capability to capture signal rhythmicity and key feature differences. After 5,000 iterations of training, the model achieved a recognition accuracy of 87% to 90%. In tests on 1,800 sets of measured data across six categories from the Xiangdong Tungsten Mine, the highest recognition rate reached 94.9%, demonstrating the method's generalization performance across different mine environments (Applied Sciences, 2026). Particularly under adverse conditions with small and imbalanced samples, the physics-informed convolutional-attention neural network and a small-sample microseismic signal recognition model integrating transfer learning, both proposed by Academician Song Zhenqi's team at Shandong University of Technology, exhibited strong robustness. Scholars are actively working to address data island challenges using data augmentation and physical prior constraints. Meanwhile, in practical engineering applications, the data processing speed of deep learning-based models has approached the second level, far exceeding manual recognition efficiency, making real-time disaster early warning feasible.

From "Seeing" to "Distinguishing": Unlocking Potential for Multi-Domain Mine Safety Applications

High-quality automatic microseismic signal recognition technology is not merely a theoretical algorithmic iteration but a key foundational technology supporting "digital mines and intelligent production."

1. Dynamic Design Optimization of Excavation and Mining Faces

By intelligently identifying microseismic events with different frequency bands and energy characteristics, mine technical personnel can accurately assess fault activation patterns and mining-induced stress migration trends. For example, the application of real-time classification models for stress distribution and fault activation in deep coal seams can quantitatively evaluate the degree of disturbance caused by mining activities on geological structures, thereby guiding targeted pressure relief measures. Practical application at the 6306 working face of the Dongtan Coal Mine demonstrated that intelligent classification can quantitatively track the coupling relationship between mining activities and geological structures, providing an actionable engineering tool for stress monitoring and early warning.

2. Early Warning Systems for Rockburst and Dynamic Disasters

In deep mining areas, precise analysis of massive microseismic data serves as the "sentry" to awaken the rockburst prevention system. A new method based on microseismic vibration field prediction and early rockburst warning exhibited excellent long-term warning capability on long-term monitoring data from the 40302 working face of a coal mine in Shaanxi Province. Furthermore, a comprehensive early warning framework integrating CNN-LSTM and CUSUM change point detection, when applied to the Dongtangzi Lead-Zinc Mine in the Qinling Mountains, reduced RMSE by approximately 30% to 56% and increased R² by about 20% to 58% compared to a standalone LSTM model. It can issue effective rock mass instability warnings one week in advance while providing a new means to quantify the stress release process after blasting.

3. Monitoring of Surface Subsidence and Mining Under Buildings, Railways, and Water Bodies

High-precision recognition can also extend to mining under surface buildings and water bodies ("three unders"). Subsequent research by Peng Fuyu from Hunan University of Science and Technology showed that CNN-based models perform excellently in identifying key microseismic events triggered by goaf subsidence. In future smart mine system planning, this technology will enable an efficient closed loop of "excavation—data—warning—feedback," promoting a transformation of mining safety from traditional "passive protection" to "active prevention and control."