|Table of Contents|

Airborne Transient Electromagnetic Noise Reduction Based on Curvelet Transform(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2023年第05期
Page:
1270-1284
Research Field:
庆贺汤中立院士从事地质工作七十周年专辑
Publishing date:

Info

Title:
Airborne Transient Electromagnetic Noise Reduction Based on Curvelet Transform
Author(s):
LIN Kai-ru1;  ZHANG Ji-feng1; 2; 3*;  ZHANG Fu-xiang1;  SHI Yu1
(1. School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China; 2. National Engineering Research Center of Offshore Oil and Gas Exploration, Beijing 100028, China; 3. Integrated Geophysical Simulation Laboratory, Chang'an University, Xi'an 710054, Shaanxi, China)
Keywords:
airborne transient electromagnetism;  curvelet transform;  noise reduction;  signal analysis;  noise interference;  fast discrete
PACS:
P631.3+25
DOI:
10.19814/j.jese.2023.02019
Abstract:
Noise interference seriously affects the data processing and imaging interpretation of aviation transient electromagnetic, and how to effectively denoise has become an important content of aviation transient electromagnetic research. Based on the characteristics of aviation transient electromagnetic noise and the multi-scale characteristics of curvelet transform, the second generation fast discrete curvelet transform(FDCT)was used to reconstruct and denoise actual aviation transient electromagnetic data. Firstly, a 3D theoretical model with anomalous bodies was established, and a direct time domain transient electromagnetic 3D simulation program was used for forward modeling. Then, three different types of noise, including random noise, square wave noise and industrial noise, were added to the theoretical electromagnetic response data, and images of different scales were generated through curvelet transform and reconstruction. Low scales reflect large background fields, while high scales reflect local details or high-frequency noise. Thirdly, the denoising effect of the reconstructed data was evaluated through parameters such as signal-to-noise ratio and relative error. Finally, the method was applied to the measured data of aviation transient electromagnetic and compared with several traditional denoising methods, including median filtering, singular value decomposition and wavelet transform. The results show that for the original data with random noise and industrial noise, the relative error after noise reduction is below 2.6%; for the original data with square wave noise, the relative error after noise reduction is below 6.5%, and the signal-to-noise ratio is also higher than traditional denoising methods, proving that the second generation curvelet transform can be applied to noise reduction of airborne transient electromagnetic data.

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Last Update: 2023-10-15