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A prediction model for microseismic signals based on kernel extreme learning machine optimized by Harris Hawks

Wei Zhu1, Yuting Bian1, Duo Lin1

  • 1School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.

Scientific Reports
|November 18, 2025
PubMed
Summary

This study introduces a new algorithm (bKSHHO-KELM) for early detection of rock hazards using microseismic and blasting signals. The method achieves high accuracy, enhancing mine safety during resource extraction.

Keywords:
Feature selectionGlobal optimizationHarris hawks optimizationMicroseismic and blasting signals

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Area of Science:

  • Geotechnical Engineering
  • Artificial Intelligence in Mining
  • Signal Processing

Background:

  • Real-time monitoring of rock stability is vital for safety in mineral extraction.
  • Microseismic and blasting signals are key early indicators of rock rupture and potential disasters.

Purpose of the Study:

  • To develop an efficient and accurate method for recognizing microseismic and blasting signals.
  • To enable early warning systems for rock hazards in mining operations.

Main Methods:

  • A binary Harris Hawks Optimization algorithm with kernel search (bKSHHO) was proposed.
  • The bKSHHO algorithm was integrated with a kernel extreme learning machine (KELM) to create the bKSHHO-KELM prediction model.
  • The KSHHO algorithm's optimization capability was validated against ten benchmark algorithms.

Main Results:

  • The bKSHHO-KELM model achieved high prediction accuracy (95.625%), recall (93.964%), precision (92.632%), and F1 score (0.931) for microseismic and blasting signals.
  • The proposed KSHHO algorithm demonstrated strong optimization capabilities.

Conclusions:

  • The bKSHHO-KELM model offers an efficient and accurate early warning solution for microseismic hazards.
  • This approach significantly enhances mine safety management by predicting potential rock instability.