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The removal of the high-frequency motion-induced noise in helicopter-borne transient electromagnetic data based on wavelet neural network
Wu, Xin1,2; Xue, Guoqiang2,3,4; Xiao, Pan1,2; Li, Jutao1; Liu, Lihua1; Fang, Guangyou1
2019
Source PublicationGEOPHYSICS
ISSN0016-8033
Volume84Issue:1Pages:K1-K9
AbstractIn helicopter-borne transient electromagnetic (HTEM) signal processing, removal of motion-induced noise is one of the most important steps. A special type of short-term noise, which could be classified as high-frequency motion-induced noise (HFM noise) based on its cause and time-frequency features, was observed in the field data of the Chinese Academy of Sciences-HTEM system. Because the HFM noise is an in-band noise for the HTEM response, it usually remains after the normal denoising procedure developed for the conventional motion-induced noise. To solve this problem, we have developed a three-stage workflow to remove the HFM noise using the wavelet neural network (WNN). In the first stage, the WNN training is performed, and the data segment in which the HFM noise is dominant is selected as the sample set. In the second stage, the HFM noise corresponding to the data segment in which the earth's response coexisted with the HFM noise is predicted using the well-trained WNN. In the last stage, the predicted HFM noise is removed from the corresponding original data. As an example, we applied our workflow in the field data observed in Inner-Mongolia, the HFM noise is removed effectively, and the results provide a strong data foundation for the subsequent processing procedures.
DOI10.1190/GEO2018-0120.1
Funding OrganizationResearch and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC)
WOS KeywordEMPIRICAL MODE DECOMPOSITION ; INVERSION
Language英语
Funding ProjectResearch and Development of the Key Instruments and Technologies for Deep Resource Prospecting[ZDYZ2012-01-03] ; National Key Research and Development Program of China[2016YFC0600101] ; National Key Research and Development Program of China[2017YFC0601204] ; Natural Science Foundation of China (NSFC)[41474095]
Funding OrganizationResearch and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; Research and Development of the Key Instruments and Technologies for Deep Resource Prospecting ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC)
WOS Research AreaGeochemistry & Geophysics
WOS SubjectGeochemistry & Geophysics
WOS IDWOS:000457601800038
PublisherSOC EXPLORATION GEOPHYSICISTS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/90494
Collection矿产资源研究院重点实验室
Corresponding AuthorWu, Xin
Affiliation1.Chinese Acad Sci, Inst Elect, Beijing 100190, Peoples R China
2.Chinese Acad Sci Univ, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Key Lab Mineral Resources, Inst Geol & Geophys, Beijing 100029, Peoples R China
4.Chinese Acad Sci, Inst Earth Sci, Beijing 100029, Peoples R China
Recommended Citation
GB/T 7714
Wu, Xin,Xue, Guoqiang,Xiao, Pan,et al. The removal of the high-frequency motion-induced noise in helicopter-borne transient electromagnetic data based on wavelet neural network[J]. GEOPHYSICS,2019,84(1):K1-K9.
APA Wu, Xin,Xue, Guoqiang,Xiao, Pan,Li, Jutao,Liu, Lihua,&Fang, Guangyou.(2019).The removal of the high-frequency motion-induced noise in helicopter-borne transient electromagnetic data based on wavelet neural network.GEOPHYSICS,84(1),K1-K9.
MLA Wu, Xin,et al."The removal of the high-frequency motion-induced noise in helicopter-borne transient electromagnetic data based on wavelet neural network".GEOPHYSICS 84.1(2019):K1-K9.
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