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Related Experiment Videos

A self-supervised GNN-Transformer framework for weak microseismic signal identification.

Mingwei Liu1, Zhigang Deng2, Yunpeng Li3

  • 1School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China.

Scientific Reports
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

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Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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A new self-supervised GNN-Transformer framework (SS-GNN-MSTF-Transformer) accurately identifies weak microseismic signals in deep mining. This robust method enhances fracture monitoring and safety assessments by overcoming complex noise challenges.

Area of Science:

  • Geophysics
  • Data Science
  • Mining Engineering

Background:

  • Weak microseismic signals are crucial for evaluating hydraulic fracturing effectiveness and ensuring mining safety.
  • Deep mining environments present challenges like strong disturbances, electromagnetic interference, and complex noise, reducing signal-to-noise ratios.
  • Traditional identification methods struggle with weak signals in noisy, complex mining conditions.

Purpose of the Study:

  • To propose a robust framework for identifying weak microseismic signals in challenging deep mining environments.
  • To enhance the accuracy, robustness, and generalization ability of microseismic signal recognition.
  • To provide a reliable solution for hydraulic fracturing monitoring, fracture propagation analysis, and safety assessment.

Main Methods:

Keywords:
Intelligent recognitionRock formation hydraulic fracturingSS-GNN-MSTF-TransformerWeak microseismic signals

Related Experiment Videos

  • Developed a self-supervised GNN-Transformer framework (SS-GNN-MSTF-Transformer).
  • Integrated a multi-scale spatiotemporal feature extraction module (MSTF) using STFT and multi-scale convolution.
  • Employed a Transformer encoder with RoPE and multi-scale attention for temporal dependencies and an adaptive GNN for spatial correlations.
  • Utilized a self-supervised learning strategy including contrastive learning, masked time-frequency prediction, and graph structure recovery.

Main Results:

  • Achieved an average accuracy of 0.9343 and F1-score under five-fold cross-validation on real microseismic data.
  • Demonstrated superior performance over conventional methods (SVM, KNN, RF, BPNN).
  • Engineering validation on independent field datasets yielded F1-scores of 0.9341 and 0.9216, showing significant improvement over RF.
  • Maintained a low inference latency of approximately 14.9 ms per sample, indicating feasibility for real-time applications.

Conclusions:

  • The proposed SS-GNN-MSTF-Transformer framework significantly improves weak microseismic signal identification accuracy, robustness, and generalization.
  • The method effectively handles complex noise and data limitations in deep mining environments.
  • The framework offers a reliable and efficient solution for critical applications in hydraulic fracturing and mining safety.