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| The application of orthogonal polynomial fitting method to airborne electromagnetic motion noise removal |
Wei HUANG1,2,3, Fang BEN1,2,3( ), Shan WU1,2,3, Si-Yuan SUN4, Gui-Xiang LIAO1,2,3, Yong-Zai XI1,2,3 |
1. Laboratory of geophysical Electromagnetic Probing Technologies, Ministry of Land and Resources, Langfang 065000, China
2. Ministry of Natural Resources, Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Science, Langfang 065000, China
3. National Research Centre of Geoexploration Technology, Langfang 065000, China
4. College of Geo-exploration Sciences and Technology, Jilin University, Changchun 130026, China |
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Abstract Time domain airborne electromagnetic measurement data are often accompanied by motion noise with low-frequency characteristics, whose amplitude is much stronger than the magnetic field intensity of the airborne electromagnetic response. Therefore, whether it can be removed correctly and efficiently has become a key technology in the pre-processing of airborne electromagnetic data in time domain. In this paper, orthogonal polynomial fitting method is used to remove the motion noise of airborne electromagnetic data in time domain. The orthogonal polynomials fitting method is used to fit each half-cycle time series so as to remove the motion noise. It can be seen from the result of removing motion noise from simulation data and real data that orthogonal polynomials fitting method can effectively remove motion noise, improve the quality of airborne electromagnetic real data, and improve the SNR of data.
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Received: 19 February 2019
Published: 15 August 2019
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Corresponding Authors:
Fang BEN
E-mail: benfangzai2008@163.com
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Comparison of motion noise suppression effects of simulation data
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Amplitude frequency characteristics of noise suppression in simulation data
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| 加入运动噪声 | 去除运动噪声 | | RMS | 6.96 | 0.29 | | SNR | 8.51 | 71.84 |
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RMS and SNR of simulation data
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Comparison of real data noise suppression
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Amplitude frequency characteristics of noise suppression in real data
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Denoising effect of different coverage points when polynomial fitting order is 2
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Amplitude frequency characteristics of different coverage points when polynomial fitting order is 2
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Denoising effect of different coverage points when polynomial fitting order is 3
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Amplitude frequency characteristics of different coverage points when polynomial fitting order is 3
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| order=2 | | k | 500 | 1000 | 1200 | 1500 | | RMS | 4.28 | 4.37 | 4.61 | 0.10 | | SNR | 22.83 | 22.45 | 21.37 | 4.46 | | order=3 | | k | 500 | 1000 | 1200 | 1500 | | RMS | 4.28 | 6.58 | 0.15 | 1.33 | | SNR | 22.89 | 14.28 | -2.85 | -45.92 |
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Denoising effect of different order and annihilation point when polynomial fitting
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