Airborne electromagnetic data denoising based on dictionary learning
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Airborne electromagnetic data denoising based on dictionary learning* Xue Shu-yang1, Yin Chang-chun1, Su Yang1, Liu Yun-he1, Wang Yong2, Liu Cai-hua3, Xiong Bin4, and Sun Huai-feng5 Abstract: Time-domain airborne electromagnetic (AEM) data are frequently subject to interference from various types of noise, which can reduce the data quality and affect data inversion and interpretation. Traditional denoising methods primarily deal with data directly, without analyzing the data in detail; thus, the results are not always satisfactory. In this paper, we propose a method based on dictionary learning for EM data denoising. This method uses dictionary learning to perform feature analysis and to extract and reconstruct the true signal. In the process of dictionary learning, the random noise is filtered out as residuals. To verify the effectiveness of this dictionary learning approach for denoising, we use a fixed overcomplete discrete cosine transform (ODCT) dictionary algorithm, the method-of-optimal-directions (MOD) dictionary learning algorithm, and the K-singular value decomposition (K-SVD) dictionary learning algorithm to denoise decay curves at single points and to denoise profile data for different time channels in time-domain AEM. The results show obvious differences among the three dictionaries for denoising AEM data, with the K-SVD dictionary achieving the best performance. Keywords: Time-domain AEM, data processing, denoising, dictionary learning, sparse representation
Introduction Airborne electromagnetic (AEM) methods are advantageous due to their high efficiency and large coverage. However, the resulting survey data are frequently affected by airflow, aircraft vibration, unstable
flight speed, and attitude changes, all of which can create strong noise. Researchers have conducted systematic analyses and have reported studies on the sources and characteristics of noise in AEM data (McCracken et al., 1984; McCracken et al., 1986; Buselli et al., 1998; Liu, 2011); moreover, various methods have been proposed
Manuscript received by the Editor December 17, 2019; received manuscript received June 11, 2020. * This paper was financially supported the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA14020102), the National Natural Science Foundation of China (Nos. 41774125, 41530320, and 41804098), and the Key National Research Project of China (Nos. 2016YFC0303100, 2017YFC0601900). 1. College of Geo-Exploration Sciences and Technology, Jilin University, Changchun 130026, China. 2. Construction Engineering College, Jilin University, Changchun 130026, China. 3. Sino Shaanxi Nuclear Industry Group 214 Brigade Co., Ltd, Xi'an 710100, China. 4. College of Earth Sciences, Guilin University of Technology, Guilin 541006, China. 5. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan 250061, China. ♦Corresponding author: Yin Chang-chun (e-mail: [email protected]). © 2020 Chinese Geophysical Society. All rights reserved.
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