Three dictionary learning algorithms and their applications for marine controlled source electromagnetic data denoising

Abstract

Marine controlled source electromagnetic (MCSEM) is profoundly used for undersea resources exploration. The effective signal is easily contaminated by kinds of noise when the transmitter-receiver offset is large. Suppressing the noise influence is vital to improve data quality and further interpretation accuracy. Denoising becomes a research focus with the widespread application of the MCSEM technique. Many denoising approaches are proposed by different researchers. However, most of them only target a single type of noise, which severely limits the application of these approaches. The fast-developing dictionary learning technique paves a new way for MCSEM data denoising. Currently, typical dictionary learning algorithms include k-means singular value decomposition (K-SVD), data-driven tight frame (DDTF), shift-invariant sparse coding (SISC) and so on. These three algorithms are different in principles and arithmetic processes. Their applications for MCSEM data denoising are explored for the first time in this article. Besides, a comparative analysis of these three noise reduction methods is carried out. The comparison proves the effectiveness and superiority of the K-SVD, followed by the DDTF method. Besides, all these denoising methods are applied to the field data. The results further corroborates the above conclusions.