摘要
随着受干扰地磁观测仪器数量的不断增多,现有半人工识别干扰事件的方法存在效率低、工作量大、识别结果因人而异等问题。本文利用2012年1月1日至2014年12月31日全国地磁台网原始观测数据和地磁专家标注的2小时内干扰事件记录,分别构建干扰事件样本和正常样本各51357条,基于卷积神经网络和自注意力机制提出一种新的干扰事件识别模型,实现干扰事件的自动、快速分类。实验结果显示,该模型在验证集的准确率达到92.93%,在测试集的准确率达到93.37%。与MLP、FCN、ResNet三种模型相比,本模型在测试集上的准确率平均提高近8.76%,表明卷积神经网络和自注意力机制等深度学习算法在地磁观测数据干扰事件识别领域具有巨大潜力,为进一步精确识别各类干扰事件探索了一种新思路。
With the continuous increase in the number of disturbed geomagnetic observation instruments,the existing semi-manual methods for identifying interference events have problems such as low efficiency,large workload,and different identification results among individuals.This paper uses the original observation data of the national geomagnetic network from January 1,2012,to December 31,2014,and the disturbance event records marked by geomagnetic experts to construct 51357 disturbance event samples and normal samples,respectively,and proposes a new interference event recognition model based on the convolutional neural networks and self-attention mechanism to realize automatic and fast classification of interference events.Experimental results show that the accuracy of the model proposed in this paper reached 92.93% in the validation set and 93.37% in the test set.Compared with the three models of MLP,FCN,and ResNet,the accuracy of this model on the test set is increased by nearly 8.76% on average,indicating that deep learning algorithms such as convolutional neural networks and self-attention mechanism have great potential in the field of identification of disturbance events of geomagnetic observation data.This is a novel idea for further accurate identification of various interference events of geomagnetic observation data.
作者
单维锋
李志扬
陈俊
刘海军
张秀霞
邢丽莉
胡秀娟
夏庆新
夏金铸
SHAN Weifeng;LI Zhiyang;CHEN Jun;LIU Haijun;ZHANG Xiuxia;XING Lili;Hu Xiujuan;XIA Qingxin;XIA Jinzhu(Institute of Disaster Prevention,Langfang 065201,China;Anhui Earthquake Agency,Hefei 230031,China;Jiangsu Earthquake Agency,Nanjing 210014,China;National Field Scientific Observation and Research Station for Huge Thick Sediments and Seismic Disasters in Hongshan,Xingtai 054000,China;School of Computer,North China Institute of Science&Technology,Langfang 065201,China)
出处
《地震地磁观测与研究》
2022年第5期49-63,共15页
Seismological and Geomagnetic Observation and Research
基金
中央高校基本科研业务费专项(项目编号:ZY20180119)
地震科技星火计划(项目编号:XH20024)
河北省地震科技星火计划项目红山野外站科研专项(项目编号:DZ2021110500003)
河北省自然科学基金(项目编号:D2022512001)
国家重点研发计划(项目编号:2018YFC1503806)。
关键词
地磁场观测数据
干扰事件识别
深度学习
卷积神经网络
自注意力机制
geomagnetic observation data
interference events identification
deep learning
convolutional neural networks
self-attention mechanism
