基于深度学习的地震震级分类

刘涛; 戴志军; 陈苏; 傅磊

doi:10.11939/jass.20210046

基于深度学习的地震震级分类

doi: 10.11939/jass.20210046

中国北京 100081 中国地震局地球物理研究所

基金项目: 国家重点研究发展计划（2018YFE0109800）和国家自然科学基金（51738001，U1839202）共同资助

详细信息

作者简介:
刘涛，在读硕士研究生，主要从事地震动记录特征分析的相关研究，e-mail： 844613078@qq.com

通讯作者:
戴志军，博士，研究员，主要从事强地震动特性分析与合成、地震动过程数值模拟、计算机视觉方法在工程地震方面的应用和深度学习相关应用研究，e-mail：dzj@cea-igp.ac.cn

中图分类号: P315.9
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- 被引次数: 0
出版历程
- 收稿日期: 2021-04-01
- 修回日期: 2021-05-31
- 网络出版日期: 2022-07-14
- 刊出日期: 2022-08-16

Earthquake magnitude classification based on deep learning

Institute of Geophysics，China Earthquake Administration，Beijing 100081，China

摘要

摘要: 为了探索地震加速度时程记录的震级信息，训练卷积神经网络基于地震震级大小对地震记录进行分类，将K-NET和KiK-net中将近12万个地震记录作为样本，对其进行信息筛选和归一化，之后将地震加速度时程记录用作输入，训练卷积神经网络模型以M5.5为分类界限来区分大震和小震。结果显示，在训练集中基于该模型的分类准确率为93.6％，在测试集中的准确率为92.3％，具有良好的分类效果，这表明大震记录与小震记录之间存在一些根本的区别，即可通过地震动加速度时程记录获取一定的震级信息。
- 深度学习 /
- 地震记录分析 /
- 卷积神经网络 /
- 震级信息
Abstract: In order to explore the magnitude information of the seismic acceleration time history recordings, we train a convolutional neural network to classify the seismic recordings based on the magnitude of the earthquakes. Nearly 120 000 earthquake recordings in K-NET and KiK-net are used as samples, and these acceleration time history recordings are used as inputs for model training after information screening and normalization. Taking the magnitude M5.5 as the classification standard, we train a deep learning model of convolutional neural network to classify large and small earthquakes. The results show that the model has an accuracy rate of 93.6% on the training set and 92.3% on the test set, which has a good classification effect. This suggests there are some fundamental differences between large earthquake recordings and small ones. Thus, earthquake magnitude information may be revealed from acceleration time history recordings of earthquakes.
- deep learning /
- seismic recording analysis /
- convoluion neural network /
- magnitude information

HTML全文

图 1 预处理阶段采样图

红线部分是采样频率为100 Hz的五个采样位置，每个位置采样时长为4 s，五个部分共20 s

Figure 1. Pre-processing sampling

The red boxes delineate the five sampling positions with a sampling frequency of 100 Hz. The sampling time of each position is 4 s，and the five parts are 20 s in total

下载: 全尺寸图片幻灯片

图 2 神经网络模型从输入数据到获取分类结果的流程图

（a）模型的流程说明；（b）模型的架构

Figure 2. Flow chart of neural network model from data inputting to classfication result acquirement

（a） Process description of the model；（b） Model architecture

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图 3 不同学习率下训练集（a）和测试集（b）的准确率随训练次数增加的变化

Figure 3. The change in the correct rates of the trainings set （a） and the test set （b） with the training time increasing on the condition of different learning rate

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图 4 不同批量大小下训练集（a）和测试集（b）的准确率随训练次数增加的变化

Figure 4. The change in the correct rates of the trainings set （a） and the test set （b） with the training time increasing on the condition of different batch size

下载: 全尺寸图片幻灯片

图 5 （a）计算模型精度的流程；（b）准确率；（c）模型对于部分记录的识别结果

Figure 5. （a） The flowchart of calculating model accuracy；（b） Training accuracy；（c） The recognition results of the model for some recordings

下载: 全尺寸图片幻灯片

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