Microseismic Event Recognition and Transfer Learning Based on Convolutional Neural Network and Attention Mechanisms

IF 0.7 4区地球科学 Q4 GEOCHEMISTRY & GEOPHYSICS Applied Geophysics Pub Date : 2024-04-02 DOI:10.1007/s11770-024-1058-y

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引用次数: 0

Abstract

Microseismic monitoring technology is widely used in tunnel and coal mine safety production. For signals generated by ultra-weak microseismic events, traditional sensors encounter limitations in terms of detection sensitivity. Given the complex engineering environment, automatic multi-classification of microseismic data is highly required. In this study, we use acceleration sensors to collect signals and combine the improved Visual Geometry Group with a convolutional block attention module to obtain a new network structure, termed CNN_BAM, for automatic classification and identification of microseismic events. We use the dataset collected from the Hanjiang-to-Weihe River Diversion Project to train and validate the network model. Results show that the CNN_BAM model exhibits good feature extraction ability, achieving a recognition accuracy of 99.29%, surpassing all its counterparts. The stability and accuracy of the classification algorithm improve remarkably. In addition, through fine-tuning and migration to the Pan II Mine Project, the network demonstrates reliable generalization performance. This outcome reflects its adaptability across different projects and promising application prospects.

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基于卷积神经网络和注意力机制的微地震事件识别与迁移学习

摘要微震监测技术被广泛应用于隧道和煤矿安全生产中。对于超弱微震事件产生的信号，传统传感器在探测灵敏度方面存在局限性。鉴于复杂的工程环境，对微地震数据进行自动多分类是非常必要的。在本研究中，我们使用加速度传感器采集信号，并将改进的视觉几何组与卷积块注意模块相结合，获得了一种新的网络结构，称为 CNN_BAM，用于自动分类和识别微震事件。我们使用从汉江至卫河引水工程收集的数据集来训练和验证网络模型。结果表明，CNN_BAM 模型具有良好的特征提取能力，识别准确率达到 99.29%，超过了所有同类模型。分类算法的稳定性和准确性也显著提高。此外，通过微调和迁移到泛二矿项目，该网络表现出可靠的泛化性能。这一成果反映了其在不同项目中的适应性和广阔的应用前景。

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来源期刊

Applied Geophysics 地学-地球化学与地球物理

CiteScore

1.50

自引率

14.30%

发文量

912

审稿时长

2 months

期刊介绍： The journal is designed to provide an academic realm for a broad blend of academic and industry papers to promote rapid communication and exchange of ideas between Chinese and world-wide geophysicists. The publication covers the applications of geoscience, geophysics, and related disciplines in the fields of energy, resources, environment, disaster, engineering, information, military, and surveying.