基于 STL-LPFMD 的 CSEM 数据去噪

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-24 DOI:10.1109/LGRS.2025.3544658

Zijie Liu;Yanfang Hu;Diquan Li

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引用次数: 0

摘要

强电磁干扰是影响电磁勘探效果的主要因素之一。本研究首次将基于黄土的季节趋势分解（STL）和低通特征模式分解（LPFMD）应用于可控源电磁法（CSEM）信号处理。我们提出的方法通过宽场电磁法（WFEM）的模拟和测量数据验证了其有效性和实用性。结果表明，STL 和 LPFMD 的结合实现了强电磁干扰的有效去除，并进一步提高了 CSEM 观测数据的信噪比（SNR）。

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CSEM Data Denoising Based on STL-LPFMD

Strong electromagnetic interference is one of the main factors affecting the effectiveness of electromagnetic exploration. In this study, the seasonal-trend decomposition based on Loess (STL) and low-pass feature mode decomposition (LPFMD) are applied to controlled-source electromagnetic method (CSEM) signal processing for the first time. The method we proposed is verified the effectiveness and practicability by the simulated and measured data of wide-field electromagnetic method (WFEM). The results show that the combination of STL and LPFMD realizes effective removal of strong electromagnetic interference and further improves the signal-to-noise ratio (SNR) of CSEM observed data.

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IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society

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