A study on sferic removal methods from actual airborne transient electromagnetic data

In airborne transient electromagnetic (ATEM) surveys, sferic significantly degrades data quality due to its randomness and uncertain energy amplitude. This study explores three methods for removing sferic: wavelet decomposition and reconstruction, α-trimmed mean filter, and Hampel filter. Their resp...

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Bibliographic Details
Published in地球与行星物理论评 Vol. 56; no. 6; pp. 674 - 681
Main Authors Ping Qi, Jiangyuan Chen, Zhihong Wang, Donghua Sun, Wei Chen, Yan Luo, Weimeng Zhang, Shasha Cheng, Lihong Peng
Format Journal Article
LanguageChinese
Published Editorial Office of Reviews of Geophysics and Planetary Physics 01.11.2025
Subjects
airborne transient electromagnetic
hampel filter
sferic
Online AccessGet full text
ISSN2097-1893
DOI10.19975/j.dqyxx.2024-042

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Summary:In airborne transient electromagnetic (ATEM) surveys, sferic significantly degrades data quality due to its randomness and uncertain energy amplitude. This study explores three methods for removing sferic: wavelet decomposition and reconstruction, α-trimmed mean filter, and Hampel filter. Their respective advantages and application scenarios in signal processing are compared. Wavelet decomposition and reconstruction offer multiscale and multiresolution signal analysis, allowing simultaneous consideration of time and frequency characteristics. The choice of basis functions affects global properties and phase shifts, while the number of decomposition levels influences data smoothing, and threshold settings impact noise removal effectiveness. α-trimmed mean filtering removes noise by averaging the remaining data after excluding the maximum and minimum values within a window, thus demonstrating adaptability. The Hampel filter detects outliers using median and median absolute deviation (MAD), effectively targeting
ISSN:2097-1893
DOI:10.19975/j.dqyxx.2024-042