基于交叉相关性的磁共振振荡信号随机噪声抑制技术

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Sensors Journal Pub Date : 2024-10-04 DOI:10.1109/JSEN.2024.3469376
Yang Zhang;Yongzhao Sun;Yue Zhou;Wenjie Hao;Tingting Lin
{"title":"基于交叉相关性的磁共振振荡信号随机噪声抑制技术","authors":"Yang Zhang;Yongzhao Sun;Yue Zhou;Wenjie Hao;Tingting Lin","doi":"10.1109/JSEN.2024.3469376","DOIUrl":null,"url":null,"abstract":"Magnetic resonance sounding (MRS) is a noninvasive geophysical method, which may detect the underground water directly. However, the weak MRS signals oscillating at Larmor frequency always suffer from low signal-to-noise ratios (SNRs) due to the multisource noise, especially the random noise. To solve this problem, a novel method for random noise suppression based on cross correlation is proposed in this manuscript. According to the characteristics of the MRS signal, a sinusoidal signal is constructed as a reference signal, which has the same frequency as Larmor frequency. It shows a strong correlation with the MRS signal, while demonstrating minimal correlation with the random noise. In terms of this property, the cross correlation is used to recover the MRS signal from random noise interference. By convolving the noisy signal with the reference signal and deconvolving the processed convolution waveform, the desired MRS signal is acquired. In order to validate the efficiency of the denoising strategy, numerical simulations on the synthetic signals embedded in different noise levels are performed, and the uncertainties of the estimated signal parameters are compared. In addition, the cross correlation method is applied following a standard processing scheme in field data, also resulting in improved SNRs. The cross correlation algorithm may achieve better denoising results than the commonly used denoising method with fewer filtering parameters and less human labor.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37463-37471"},"PeriodicalIF":4.3000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Random Noise Suppression of Magnetic Resonance Sounding Oscillating Signal Based on Cross Correlation\",\"authors\":\"Yang Zhang;Yongzhao Sun;Yue Zhou;Wenjie Hao;Tingting Lin\",\"doi\":\"10.1109/JSEN.2024.3469376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Magnetic resonance sounding (MRS) is a noninvasive geophysical method, which may detect the underground water directly. However, the weak MRS signals oscillating at Larmor frequency always suffer from low signal-to-noise ratios (SNRs) due to the multisource noise, especially the random noise. To solve this problem, a novel method for random noise suppression based on cross correlation is proposed in this manuscript. According to the characteristics of the MRS signal, a sinusoidal signal is constructed as a reference signal, which has the same frequency as Larmor frequency. It shows a strong correlation with the MRS signal, while demonstrating minimal correlation with the random noise. In terms of this property, the cross correlation is used to recover the MRS signal from random noise interference. By convolving the noisy signal with the reference signal and deconvolving the processed convolution waveform, the desired MRS signal is acquired. In order to validate the efficiency of the denoising strategy, numerical simulations on the synthetic signals embedded in different noise levels are performed, and the uncertainties of the estimated signal parameters are compared. In addition, the cross correlation method is applied following a standard processing scheme in field data, also resulting in improved SNRs. The cross correlation algorithm may achieve better denoising results than the commonly used denoising method with fewer filtering parameters and less human labor.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"24 22\",\"pages\":\"37463-37471\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10704994/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10704994/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

磁共振探测(MRS)是一种非侵入式地球物理方法,可直接探测地下水。然而,由于多源噪声,尤其是随机噪声,在拉莫尔频率振荡的微弱 MRS 信号总是存在信噪比(SNR)低的问题。为解决这一问题,本手稿提出了一种基于交叉相关的新型随机噪声抑制方法。根据 MRS 信号的特点,构建了一个与 Larmor 频率相同的正弦信号作为参考信号。它与 MRS 信号的相关性很强,而与随机噪声的相关性很小。根据这一特性,交叉相关被用来从随机噪声干扰中恢复 MRS 信号。通过将噪声信号与参考信号卷积,并对处理后的卷积波形进行解卷积,就能获得所需的 MRS 信号。为了验证去噪策略的效率,对嵌入不同噪声水平的合成信号进行了数值模拟,并比较了估计信号参数的不确定性。此外,交叉相关方法按照标准处理方案应用于现场数据,也提高了信噪比。与常用的去噪方法相比,交叉相关算法可以用更少的滤波参数和更少的人力获得更好的去噪效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Random Noise Suppression of Magnetic Resonance Sounding Oscillating Signal Based on Cross Correlation
Magnetic resonance sounding (MRS) is a noninvasive geophysical method, which may detect the underground water directly. However, the weak MRS signals oscillating at Larmor frequency always suffer from low signal-to-noise ratios (SNRs) due to the multisource noise, especially the random noise. To solve this problem, a novel method for random noise suppression based on cross correlation is proposed in this manuscript. According to the characteristics of the MRS signal, a sinusoidal signal is constructed as a reference signal, which has the same frequency as Larmor frequency. It shows a strong correlation with the MRS signal, while demonstrating minimal correlation with the random noise. In terms of this property, the cross correlation is used to recover the MRS signal from random noise interference. By convolving the noisy signal with the reference signal and deconvolving the processed convolution waveform, the desired MRS signal is acquired. In order to validate the efficiency of the denoising strategy, numerical simulations on the synthetic signals embedded in different noise levels are performed, and the uncertainties of the estimated signal parameters are compared. In addition, the cross correlation method is applied following a standard processing scheme in field data, also resulting in improved SNRs. The cross correlation algorithm may achieve better denoising results than the commonly used denoising method with fewer filtering parameters and less human labor.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
期刊最新文献
IEEE Sensors Journal Publication Information Table of Contents Front Cover IEEE Sensors Council A Clustered Routing Algorithm Based on Forwarding Mechanism Optimization
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1