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首页> 外文期刊>Seismological research letters >Comparison of Single‐Trace and Multiple‐Trace Polarity Determination for Surface Microseismic Data Using Deep Learning
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Comparison of Single‐Trace and Multiple‐Trace Polarity Determination for Surface Microseismic Data Using Deep Learning

机译：深度学习对表面微震数据的单迹和多径极性测定的比较

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摘要

For surface microseismic monitoring, determination of the P‐wave first‐motion polarity is important because (1)?it has been widely used to determine focal mechanisms and (2)?the location accuracy of the diffraction‐stack‐based method is improved greatly using polarization correction. The convolutional neural network (CNN) is a form of deep learning algorithm that can be applied to predict the polarity of a seismogram automatically. However, the existing network designed for polarity detection utilizes only individual trace information. In this study, we design a multitrace‐based CNN (MT‐CNN) architecture using several neighbor traces combined as training samples, which could utilize the polarity information of neighbor sensors in the surface microseismic array. We use 17,227 field seismograms with labeled polarities to train two different neural networks that predict the polarities by a single trace or by multiple traces. The performance of the test set and field example of two CNN architectures shows that the MT‐CNN significantly produces fewer polarity prediction errors and leads to more accurate focal mechanism solutions for microseismic events.
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  • 来源
    《Seismological research letters》 |2020年第3期|共10页
  • 作者

    Xiao Tian; Wei Zhang; Xiong Zhang; Jie Zhang; Qingshan Zhang; Xiangteng Wang; Quanshi Guo;

  • 作者单位

    Fundamental Science on Radioactive Geology and Exploration Technology Laboratory East China University of Technology;

    Department of Earth and Space Sciences Southern University of Science and Technology;

    Fundamental Science on Radioactive Geology and Exploration Technology Laboratory East China University of Technology;

    School of Earth and Space Sciences University of Science and Technology of China;

    School of Earth and Space Sciences University of Science and Technology of China;

    Fundamental Science on Radioactive Geology and Exploration Technology Laboratory East China University of Technology;

    Sinopec Geophysical Research Institute;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地震学;
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