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Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

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Published on: August 7, 2017

High-Precision Coal Mine Microseismic P-Wave Arrival Picking via Physics-Constrained Deep Learning.

Kai Qin^1,2, Zhigang Deng^1,2, Xiaohan Li¹

¹China Coal Research Institute, Beijing 100013, China.

Sensors (Basel, Switzerland)

|December 11, 2025

Summary

This summary is machine-generated.

A new deep learning model accurately identifies P-wave arrival times in microseismic signals for coal mine safety. This advanced method improves early warning systems for dynamic hazards, outperforming traditional techniques.

Keywords:

arrival time retrieval deep learning event detection microseismic monitoring

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Area of Science:

Geophysics
Mining Engineering
Artificial Intelligence

Background:

Accurate P-wave arrival time identification is vital for intelligent monitoring and early warning of dynamic hazards in coal mines.
Traditional methods struggle with accuracy and stability due to complex geology and noise, limiting their effectiveness.

Purpose of the Study:

To develop and validate a novel microseismic P-wave arrival time automatic picking model integrating physical constraints and deep learning.
To enhance the precision, robustness, and automation of microseismic monitoring systems in coal mines.

Main Methods:

A deep learning model was designed, incorporating physical constraints for microseismic P-wave arrival time picking.
The model was trained and optimized using a high-quality, manually labeled dataset.
Performance was systematically compared against traditional methods like AR picker and STA/LTA.

Main Results:

The proposed model achieved high precision (96.60%), recall (90.59%), and F1 score (93.50%) on the test set.
The average P-wave arrival time picking error was significantly reduced to 5.49 ms, well below the 20 ms threshold.
The model demonstrated good engineering transferability in similar mining environments but showed sensitivity to varying data acquisition parameters.

Conclusions:

The developed model offers a high-precision, robust solution for automatic microseismic P-wave arrival time picking.
This advancement supports the automation and intelligence of coal mine microseismic monitoring, crucial for real-time hazard prevention.
The model holds practical value for improving early warning systems and risk management in mining operations.