VMD-SE-CEEMDAN-BO-CNNGRU: A Dual-Stage Mode Decomposition Hybrid Deep Learning Model for Microseismic Time Series Prediction

Mingyi Cui (), Enke Hou () and Pengfei Hou
Additional contact information
Mingyi Cui: College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
Enke Hou: College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
Pengfei Hou: College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China

Mathematics, 2025, vol. 13, issue 13, 1-35

Abstract: Coal mine disaster safety monitoring often employs microseismic technology for its high sensitivity and real-time capability. However, nonlinear, non-stationary, and multi-scale signals limit traditional time series models (e.g., ARMA, ARIMA). This paper proposes a hybrid deep learning model—VMD-SE-CEEMDAN-BO-CNNGRU—integrating variational mode decomposition, sample entropy, CEEMDAN, Bayesian optimization, and a CNN-GRU architecture. Microseismic data from the 08 working face in D mine (Weibei mining area) were used to predict daily maximum energy, average energy, and frequency. The model achieved high predictive performance with R 2 values of 0.93, 0.89, and 0.88, significantly outperforming baseline models lacking modal decomposition. Comparative experiments verified the superiority of the VMD-first, SE-reconstruction, and CEEMDAN-second decomposition strategy, yielding up to 13% greater accuracy than reverse-order schemes. The model maintained R 2 above 0.80 on another dataset from the 03 working face in W mine (Binchang mining area), demonstrating robust generalization. Although performance declined during fault disturbances, accuracy for average energy and frequency rebounded post-disturbance, indicating strong adaptability. Overall, the VSCB-CNNGRU model enhances both accuracy and stability in microseismic prediction, supporting dynamic risk assessment and early warning in coal mining.

Keywords: microseismic monitoring; dual-stage modal decomposition; deep learning; coal mine safety; intelligent coal mine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/13/2121/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/13/2121/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:13:p:2121-:d:1690110

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().