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Waveform recognition and process interpretation of microseismic monitoring based on an improved LeNet5 convolutional neural network

基于改进LeNet5卷积神经网络的微震监测波形识别与过程解释

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Abstract

The development of high-precision and interpretable automatic waveform classification algorithms with strong adaptability is becoming increasingly significant under the background of the big data era of microseismicity. Considering the deficiency of the existing network in waveform recognition and classification, an improved model which is suitable for microseismic (MS) monitoring waveform recognition was proposed in this study based on the LeNet framework. The improved model was applied to investigate thirteen kinds of MS monitoring signals that appear within 8 months of the Hanjiang-to-Weihe River Diversion Project. The results show that the accuracy of the best framework in the improved model is 0.98, which is 0.1 higher than original model. The average precision, recall and F1 values of all improved models increased by 0.11, 0.12 and 0.12, respectively. Meanwhile, the improved model can visualize the entire waveform recognition process. A novel observation is that in some signal categories, the improved model mainly classified by focusing on the background information instead of the waveforms. It provides a reference for the intelligent classification of signals in MS monitoring engineering.

摘要

在微震大数据时代背景下, 开发高精度、可解释、适应性强的波形自动分类算法变得越来越重要。针对现有网络波形识别和分类的不足, 基于LeNet 框架提出了一种适用于微震监测波形识别的改进模型。应用改进后的模型对引汉济渭工程8 个月内出现的13 种微震监测信号进行了研究。结果表明, 改进模型中最佳框架的精度为0.98, 比原模型提高了0.10。所有改进模型的平均精确度、召回率和F1值分别提高了0.11、0.12 和0.12。同时, 改进后的模型可以对整个波形的识别过程可视化。在某些信号类别中, 改进的模型主要通过关注背景信息而不是波形来分类, 为微震监测工程中信号的智能分类提供了参考。

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Authors and Affiliations

  1. State Key Laboratory of Coastal and Offshore Engineering (Dalian University of Technology), Dalian, 116024, China

    Jia-ming Li  (李佳明) & Shi-bin Tang  (唐世斌)

  2. Research and Development Center of Transport Industry of Intelligent Manufacturing Technologies of Transport Infrastructure, CCCC Second Harbor Engineering Company Ltd., Wuhan, 430000, China

    Fang-wen Weng  (翁方文) & Kun-yao Li  (李焜耀)

  3. Shanxi Coking Coal Xishan Coal and Electricity Group, Taiyuan, 030053, China

    Hua-wei Yao  (要华伟)

  4. State Key Laboratory of Coal Resources and Safe Mining (China University of Mining and Technology), Xuzhou, 221116, China

    Qing-yuan He  (何青源)

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  1. Jia-ming Li  (李佳明)
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  2. Shi-bin Tang  (唐世斌)
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Correspondence to Shi-bin Tang  (唐世斌).

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Contributors

LI Jia-ming wrote the manuscript. TANG Shi-bin provided suggestions for the research method and revised the article completely. WENG Fang-wen and LI Kun-yao calculated and analyzed the signal data. YAO Hua-wei and HE Qing-yuan processed the basic data.

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Projects(51874065, U1903112, 41941018) supported by the National Natural Science Foundation of China

Conflict of interest

LI Jia-ming, TANG Shi-bin, WENG Fang-wen, LI Kun-yao, YAO Hua-wei and HE Qing-yuan declare that they have no conflict of interest.

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Li, Jm., Tang, Sb., Weng, Fw. et al. Waveform recognition and process interpretation of microseismic monitoring based on an improved LeNet5 convolutional neural network. J. Cent. South Univ. 30, 904–918 (2023). https://doi.org/10.1007/s11771-023-5254-3

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