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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

Summary

This summary is machine-generated.

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 selection Global optimization Harris hawks optimization Microseismic 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.