Suppression of motion noise based on a linear‐homomorphic filtering algorithm in airborne electromagnetic survey. (19th April 2021)

• Record Type: Journal Article
• Authors: Zhu, Kaiguang; Peng, Cong; Jing, Chunyang; Fan, Tianjiao; Yang, Yang
• Is Part Of: Geophysical prospecting. Volume 69:Number 5(2021)
• Page Start: 1102
• Page End: 1115
• Publication Date: 2021-04-19
• Subjects: Electromagnetics -- Data processing -- Noise; Prospecting -- Geophysical methods -- Periodicals; 622.15
• DOI: 10.1111/1365-2478.13078
• Languages: English
• ISSNs: 0016-8025

Abstract

ABSTRACT: The movement of the receiver coil in a magnetic field produces motion noise in the airborne electromagnetic field. With an amplitude that is several orders of magnitude higher than that of other noise sources, this motion noise is one of the main low‐frequency (<1 kHz) noise sources. Especially in the late decay period, the residual motion noise produces false anomalous information, which is a key problem of low‐base‐frequency airborne electromagnetic surveys. In this paper, a method for calculating the motion noise is proposed. This method takes into account both the earth's magnetic field and the active secondary magnetic field and introduces additive and multiplicative motion noise. Then, a linear‐homomorphic filtering algorithm is proposed to handle the additive and multiplicative motion noises. In the linear filtering part of our algorithm, the minimum noise fraction transform is applied to suppress additive motion noise. It provides sets of components ordered by the signal‐to‐noise ratio by deriving the eigenvalues and eigenvectors for the signal and uncorrelated noise covariance matrices. In the homomorphic filtering part, the multiplicative motion noise is removed. To do this, the adaptive‐width smoothing algorithm is applied to the logarithm of the filtered with the linear filtering process profile data. The results of synthetic and field data demonstrate that the linear filtering process based on the minimum noise fraction transform is effective for very low signal‐to‐noise ratios of the late‐time data, and the homomorphic filtering process can help distinguish small anomalies of these channels.