Airborne electromagnetic data denoising based on dictionary learning*
Xue Shu-yang 1, Yin Chang-chun 1, Su Yang 1, Liu Yun-he 1, Wang Yong 2, Liu Cai-hua 3, Xiong Bin 4, and Sun Huai-feng 5
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.
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.
2020, Vol. 17 
