Research on microseismic signal identification through data fusion

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xingli Zhang, Zihan Zhang, Ruisheng Jia, Xinming Lu
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引用次数: 0

Abstract

The present study proposes a double-branch classification network, DPNet (Double Path Net), for the classification and identification of microseismic and blasting signals based on multimodal feature extraction. The vibration signals’ one-dimensional spectrogram and two-dimensional wavelet time–frequency graph are inputted into the double branch network. Subsequently, convolutional neural networks and ResNet are employed to extract the one-dimensional frequency features and two-dimensional time–frequency features of the vibration signals, respectively. Experimental results demonstrate that our proposed method achieves outstanding classification performance with an accuracy of 97.34% for microseismic signals and blasting signals. This research not only provides innovative solutions to practical problems but also introduces a novel idea of multimodal feature extraction at a theoretical level. By successfully applying it to efficiently classify complex signals in mining engineering, we offer a feasible solution that holds promising prospects for practical applications in this field.

通过数据融合识别微地震信号的研究
本研究提出了一种基于多模态特征提取的双分支分类网络 DPNet(Double Path Net),用于微震和爆破信号的分类和识别。振动信号的一维频谱图和二维小波时频图被输入双分支网络。然后,利用卷积神经网络和 ResNet 分别提取振动信号的一维频率特性和二维时频特征。实验结果表明,我们提出的方法在微震信号和爆破信号的分类上取得了出色的成绩,准确率高达 97.34%。这项研究不仅为实际问题提供了创新性的解决方案,还在理论层面引入了多模态特征提取的新思路。通过将其成功应用于采矿工程中复杂信号的高效分类,我们提供了一种可行的解决方案,在该领域的实际应用中前景广阔。
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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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