Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Enhancing safety and early warning capabilities in mining through microseismic monitoring technology
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 17 March 2026

Enhancing safety and early warning capabilities in mining through microseismic monitoring technology

  • Jiaxu Jin1,
  • Yong Xiao1,
  • Pengfei Wu1,
  • Jun Wang2,
  • Jie Liu3,
  • Yunlong Mo4,5 &
  • …
  • Lei Zhang6 

Scientific Reports , Article number:  (2026) Cite this article

  • 429 Accesses

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Environmental sciences
  • Natural hazards
  • Solid Earth sciences

Abstract

In the mining industry, microseismic monitoring has emerged as a crucial instrument for reducing risks related to the stability of underground rock masses. The present research conducts an analysis of dynamic hazards specifically at the 8204-2 working face in Tashan Mine. A novel monitoring framework is introduced, which integrates a nonlinear threshold curve model with a wireless microseismic monitoring system. The results demonstrate the effectiveness of the microseismic monitoring system with high-frequency sampling, utilizing the nonlinear threshold model, in detecting and providing real-time early warnings for microseismic signals, even in challenging geological environments. Practical implementation and monitoring over the past six months revealed that the microseismic monitoring system, employing the threshold curve model, achieved a monitoring and early warning accuracy exceeding 95%. This model combines the construction of a nonlinear threshold curve with dynamic conversion of confidence factors, and implements multi-level graded early warning based on the matching degree between real-time data and the hazard model, making its accuracy superior to traditional monitoring and early warning systems. Consequently, this study holds great significance in enhancing precise identification and early warning capabilities for mine microseism, as well as improving safety assessments for deep rock mining and construction practices.

Similar content being viewed by others

A dual branch model for predicting microseismic magnitude time series named DTFNet

Article Open access 13 March 2025

Event recognition technology and short-term rockburst early warning model based on microseismic monitoring and ensemble learning

Article Open access 28 May 2025

Characterisation of precursory seismic activity towards early warning of landslides via semi-supervised learning

Article Open access 06 January 2025

Data availability

The authors confirm that the data supporting the finding of this study are available within the article.

References

  1. Wang, C., Zhao, X., Zhu, Q., Yu, W. & Wu, T. Microseismic source location method and microseismic event source parameter characteristic analysis for surface microseismic system. Min. Metall. Explor. 42(5), 2963–2980. https://doi.org/10.1007/s42461-025-01322-0 (2025).

    Google Scholar 

  2. Vafaei Shoushtari, S., Giroux, B., Gloaguen, E. & Nasr, M. Detection of mining-induced microseismicity through a deep convolutional neural network. J. Appl. Geophys. https://doi.org/10.1016/j.jappgeo.2025.106069 (2025).

    Google Scholar 

  3. Zhang, X., Mao, Q., Yu, R. & Jia, R. A multivariate time series prediction model for microseismic characteristic data in coal mines. J. Appl. Geophys. 236, 105683. https://doi.org/10.1016/j.jappgeo.2025.105683 (2025).

    Google Scholar 

  4. Lai, X. et al. Microseismic energy distribution and impact risk analysis of complex heterogeneous spatial evolution of extra-thick layered strata. Sci. Rep. 12(1), 10832. https://doi.org/10.1038/s41598-022-14538-7 (2022).

    Google Scholar 

  5. Liu, J., Si, Y., Wei, D., Shi, H. & Wang, R. Developments and prospects of microseismic monitoring technology in underground metal mines in China. J. Cent. South. Univ. 28(10), 3074–3098. https://doi.org/10.1007/s11771-021-4839-y (2021).

    Google Scholar 

  6. Cui, F. et al. Study on rule of overburden failure and rock burst hazard under repeated mining in fully mechanized top-coal caving face with hard roof. Energies https://doi.org/10.3390/en12244780 (2019).

    Google Scholar 

  7. Ge, M. Efficient mine microseismic monitoring. Int. J. Coal Geol. 64(1–2), 44–56. https://doi.org/10.1016/j.coal.2005.03.004 (2005).

    Google Scholar 

  8. Liang, Z., Xue, R., Xu, N. & Li, W. Characterizing rockbursts and analysis on frequency-spectrum evolutionary law of rockburst precursor based on microseismic monitoring. Tunn. Undergr. Space Technol. https://doi.org/10.1016/j.tust.2020.103564 (2020).

    Google Scholar 

  9. Liu, C., Xue, J., Yu, G. & Cheng, X. Fractal characterization for the mining crack evolution process of overlying strata based on microseismic monitoring technology. Int. J. Min. Sci. Technol. 26(2), 295–299. https://doi.org/10.1016/j.ijmst.2015.12.016 (2016).

    Google Scholar 

  10. Ma, T. et al. Microseismic monitoring, positioning principle, and sensor layout strategy of rock mass engineering. Geofluids https://doi.org/10.1155/2020/8810391 (2020).

    Google Scholar 

  11. Ma, T. et al. Characteristics of rockburst and early warning of microseismic monitoring at Qinling water tunnel. Geomat. Nat. Hazards Risk 13(1), 1366–1394. https://doi.org/10.1080/19475705.2022.2073830 (2022).

    Google Scholar 

  12. Ma, T., Tang, C., Liu, F., Zhang, S. & Feng, Z. Microseismic monitoring, analysis and early warning of rockburst. Geomat. Nat. Hazards Risk 12(1), 2956–2983. https://doi.org/10.1080/19475705.2021.1968961 (2021).

    Google Scholar 

  13. Ma, T.-H. et al. Rockburst mechanism and prediction based on microseismic monitoring. Int. J. Rock Mech. Min. Sci. 110, 177–188. https://doi.org/10.1016/j.ijrmms.2018.07.016 (2018).

    Google Scholar 

  14. Dong, L., Li, X. & Peng, K. Prediction of rockburst classification using Random Forest. Trans. Nonferrous Met. Soc. China. 23(2), 472–477. https://doi.org/10.1016/s1003-6326(13)62487-5 (2013).

    Google Scholar 

  15. Ding, L., Chen, Z., Pan, Y. & Song, B. Mine microseismic time series data integrated classification based on improved wavelet decomposition and ELM. Cogn. Comput. 14(4), 1526–1546. https://doi.org/10.1007/s12559-022-09997-z (2022).

    Google Scholar 

  16. Jiang, W., Ding, W., Zhu, X. & Hou, F. A recognition algorithm of seismic signals based on wavelet analysis. J. Mar. Sci. Eng. https://doi.org/10.3390/jmse10081093 (2022).

    Google Scholar 

  17. Zhang, J., Jiang, R., Li, B. & Xu, N. An automatic recognition method of microseismic signals based on EEMD-SVD and ELM. Comput. Geosci. https://doi.org/10.1016/j.cageo.2019.104318 (2019).

    Google Scholar 

  18. Zhang, X., Zhao, Z., Jia, R. & Cao, L. Identification of microseismic signals based on multiscale singular spectrum entropy. Shock Vib. 2020, 1–12. https://doi.org/10.1155/2020/6717128 (2020).

    Google Scholar 

  19. Peng, K., Tang, Z., Dong, L. & Sun, D. Machine learning based identification of microseismic signals using characteristic parameters. Sensors https://doi.org/10.3390/s21216967 (2021).

    Google Scholar 

  20. Li, Q., Li, Y. & He, Q. Mine-microseismic-signal recognition based on LMD–PNN method. Appl. Sci. https://doi.org/10.3390/app12115509 (2022).

    Google Scholar 

  21. Zhang, Z., Sun, J. & Zhang, H. Nonlinear evolution characteristics of the mutual-exciting network of acoustic emission events and the multifractal early warning model. Fractals https://doi.org/10.1142/S0218348X26500337 (2026).

    Google Scholar 

  22. He, S., Qin, M., Qiu, L., Song, D. & Zhang, X. Early warning of coal dynamic disaster by precursor of AE and EMR “quiet period”. Int. J. Coal Sci. Technol. https://doi.org/10.1007/s40789-022-00514-z (2022).

    Google Scholar 

  23. Jiskani, I. M. et al. Overcoming mine safety crisis in Pakistan: An appraisal. Process Saf. Prog. https://doi.org/10.1002/prs.12041 (2019).

    Google Scholar 

  24. Huo, Y., Zhang, W., Zhang, J. & Yang, H. Using microseismic events to improve the accuracy of sensor orientation for downhole microseismic monitoring. Geophys. Prospect. 69(6), 1167–1180. https://doi.org/10.1111/1365-2478.13099 (2021).

    Google Scholar 

  25. Dou, L., Cai, W., Cao, A. & Guo, W. Comprehensive early warning of rock burst utilizing microseismic multi-parameter indices. Int. J. Min. Sci. Technol. 28(5), 767–774. https://doi.org/10.1016/j.ijmst.2018.08.007 (2018).

    Google Scholar 

  26. Yu, Q. et al. Multivariate early warning method for rockburst monitoring based on microseismic activity characteristics. Front. Earth Sci. https://doi.org/10.3389/feart.2022.837333 (2022).

    Google Scholar 

  27. Wang, X., Liu, W., Jiang, X., Zhang, Q. & Wei, Y. Evolution characteristics of overburden instability and failure under deep complex mining conditions. Geofluids 2022, 1–16. https://doi.org/10.1155/2022/6418082 (2022).

    Google Scholar 

  28. Xia, X., Chen, Z. & Wei, W. Research on monitoring and prewarning system of accident in the coal mine based on big data. Sci. Program. 2018, 1–10. https://doi.org/10.1155/2018/9308742 (2018).

    Google Scholar 

  29. Shi, Y. et al. A study on enhancing the accuracy of P wave detection based on double-filtering algorithm for microseismic signals from underground mines. IOP Conf. Ser. Earth Environ. Sci. https://doi.org/10.1088/1755-1315/440/5/052095 (2020).

    Google Scholar 

  30. Wei, H., Shu, W., Dong, L., Huang, Z. & Sun, D. A waveform image method for discriminating micro-seismic events and blasts in underground mines. Sensors https://doi.org/10.3390/s20154322 (2020).

    Google Scholar 

  31. Niu, D. et al. A machine-learning approach combining wavelet packet denoising with Catboost for weather forecasting. Atmosphere https://doi.org/10.3390/atmos12121618 (2021).

    Google Scholar 

  32. Huang, G.-B., Zhu, Q.-Y. & Siew, C.-K. Extreme learning machine: Theory and applications. Neurocomputing 70(1–3), 489–501. https://doi.org/10.1016/j.neucom.2005.12.126 (2006).

    Google Scholar 

  33. Curilem, M. et al. Pattern recognition applied to seismic signals of the Llaima Volcano (Chile): An analysis of the events’ features. J. Volcanol. Geotherm. Res. 282, 134–147. https://doi.org/10.1016/j.jvolgeores.2014.06.004 (2014).

    Google Scholar 

  34. Orlic, N. & Loncaric, S. Earthquake—explosion discrimination using genetic algorithm-based boosting approach. Comput. Geosci. 36(2), 179–185. https://doi.org/10.1016/j.cageo.2009.05.006 (2010).

    Google Scholar 

  35. Vallejos, J. A. & McKinnon, S. D. Logistic regression and neural network classification of seismic records. Int. J. Rock Mech. Min. Sci. 62, 86–95. https://doi.org/10.1016/j.ijrmms.2013.04.005 (2013).

    Google Scholar 

Download references

Funding

Supported by the National Natural Science Foundation of China (Grants. 52274206; 52404086). Supported by State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering (SDGZK2420).

Author information

Authors and Affiliations

  1. School of Civil Engineering, Liaoning Technical University, Fuxin, 123000, Liaoning, China

    Jiaxu Jin, Yong Xiao & Pengfei Wu

  2. State Key Laboratory of Deep Earth Engineering Intelligent Construction and Healthy Operation and Maintenance, Shenzhen University, Shenzhen, 518060, Guangdong, China

    Jun Wang

  3. Northeastern University, Shenyang, 110819, Liaoning, China

    Jie Liu

  4. China Coal Research Institute, Beijing, 100013, China

    Yunlong Mo

  5. State Key Laboratory of Coal Mine Disaster Prevention and Control, Beijing, 100013, China

    Yunlong Mo

  6. Machinery Technology Development Co., Ltd., Beijing, 100190, China

    Lei Zhang

Authors
  1. Jiaxu Jin
    View author publications

    Search author on:PubMed Google Scholar

  2. Yong Xiao
    View author publications

    Search author on:PubMed Google Scholar

  3. Pengfei Wu
    View author publications

    Search author on:PubMed Google Scholar

  4. Jun Wang
    View author publications

    Search author on:PubMed Google Scholar

  5. Jie Liu
    View author publications

    Search author on:PubMed Google Scholar

  6. Yunlong Mo
    View author publications

    Search author on:PubMed Google Scholar

  7. Lei Zhang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

The concept and design, Jiaxu Jin; Microseismic disaster early warning model, Jie Liu and Jun Wang; Microseismic monitoring system development, Pengfei Wu; Data collection and dynamic disaster early warning system application analysis Yunlong Mo and Lei Zhang; Writing–original draft, Yong Xiao; Writing–review & editing, Yong Xiao.

Corresponding author

Correspondence to Jun Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, J., Xiao, Y., Wu, P. et al. Enhancing safety and early warning capabilities in mining through microseismic monitoring technology. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43781-5

Download citation

  • Received: 01 February 2026

  • Accepted: 06 March 2026

  • Published: 17 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-43781-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Deep mine
  • Dynamic disaster
  • Microseismic monitoring
  • Early warning
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing