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Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel

Hang Zhang^1,2, Jun Zeng¹, Chunchi Ma^1,3

¹State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China.

Sensors (Basel, Switzerland)

|October 26, 2021

Summary

This summary is machine-generated.

This study introduces a microseismic multi-classification (MMC) model using short-time Fourier transform and convolutional neural networks. The model accurately classifies microseismic data, improving signal processing and rock mass stability analysis.

Keywords:

convolutional neural network microseismic waveforms multi-classification similarity

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Area of Science:

Geophysics
Signal Processing
Machine Learning

Background:

Microseismic data analysis requires accurate multi-classification due to complex waveforms.
Existing methods face challenges in distinguishing microseismic signals from noise.
Precise classification is crucial for signal processing and rock mass stability assessments.

Purpose of the Study:

To propose a novel microseismic multi-classification (MMC) model.
To enhance the accuracy and robustness of microseismic data classification.
To demonstrate the model's effectiveness in various geological conditions.

Main Methods:

Utilizing short-time Fourier transform (STFT) for feature extraction.
Employing convolutional neural networks (CNN) for data classification.
Inputting real and imaginary parts of STFT coefficients into the CNN model.

Main Results:

The proposed MMC model achieves optimal performance in Precision, Recall, and F1-score.
The model exhibits low sensitivity to noise, validated using semi-synthetic data.
Accurate detection of microseismic signals with M ≥ 0.2 is achieved across diverse geological settings.

Conclusions:

The MMC model offers superior performance for microseismic data multi-classification.
The method demonstrates robustness against noise and generalizability across geological conditions.
Potential applications include exploration seismology and earthquake studies.