scholarly journals IMF-Slices for GPR Data Processing Using Variational Mode Decomposition Method

2018 ◽  
Vol 10 (3) ◽  
pp. 476 ◽  
Author(s):  
Xuebing Zhang ◽  
Enhedelihai Nilot ◽  
Xuan Feng ◽  
Qianci Ren ◽  
Zhijia Zhang
Keyword(s):  
Data Processing ◽  
Decomposition Method ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

scholarly journals Processing and Interpretation of UAV Magnetic Data: A Workflow Based on Improved Variational Mode Decomposition and Levenberg-Marquardt Algorithm

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Yaoxin Zheng ◽  
Shiyan Li ◽  
Kang Xing ◽  
Xiaojuan Zhang
Keyword(s):  
Data Processing ◽  
Original Data ◽  
Magnetic Data ◽  
Variational Mode Decomposition ◽  
Euler Deconvolution ◽  
Near Surface ◽  
Mode Decomposition ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Lm Algorithm

Unmanned aerial vehicles (UAVs) have become a research hotspot in the field of magnetic exploration because of their unique advantages, e.g., low cost, high safety, and easy to operate. However, the lack of effective data processing and interpretation method limits their further deployment. In view of this situation, a complete workflow of UAV magnetic data processing and interpretation is proposed in this paper, which can be divided into two steps: (1) the improved variational mode decomposition (VMD) is applied to the original data to improve its signal-to-noise ratio as much as possible, and the decomposition modes number K is determined adaptively according to the mode characteristics; (2) the parameters of target position and magnetic moment are obtained by Euler deconvolution first, and then used as the prior information of the Levenberg–Marquardt (LM) algorithm to further improve its accuracy. Experiments are carried out to verify the effectiveness of the proposed method. Results show that the proposed method can significantly improve the quality of the original data; by combining the Euler deconvolution and LM algorithm, the horizontal positioning error can be reduced from 15.31 cm to 4.05 cm, and the depth estimation error can be reduced from 16.2 cm to 5.4 cm. Moreover, the proposed method can be used not only for the detection and location of near-surface targets, but also for the follow-up work, such as the clearance of targets (e.g., the unexploded ordnance).

Improved variational mode decomposition method for vibration signal processing of flood discharge structure

2021 ◽  
pp. 107754632110161
Author(s):  
Huokun Li ◽  
Gang Wang ◽  
Bowen Wei ◽  
Hanyue Liu ◽  
Wei Huang
Keyword(s):  
High Frequency ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Structural Vibration ◽  
Variational Mode Decomposition ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Discharge Structure ◽  
Frequency Components ◽  
Structure Vibration

It is crucial for flood discharge structure vibration safety evaluations to filter low-frequency noise, separate dense-frequency components and obtain high-frequency component accurately from vibration signals. Variational mode decomposition, a novel signal adaptive decomposition method, effectively processes flood discharge structures. However, the mode number and quadratic penalty item uncertainty in variational mode decomposition directly affects the vibration signal decomposition. Therefore, an improved variational mode decomposition method for vibration signal processing is proposed in this study. The proposed method adaptively determines the mode number based on singular entropy and frequency stability to completely separate the structural vibration components (including dense-frequency components and high-frequency components) and noise components from the vibration signal. Next, an objective quadratic penalty item function based on sample entropy and mutual information is proposed to quantify the mode mixing between the structural vibration components. Finally, a particle swarm optimisation algorithm based on beetle antenna search is proposed to optimise the quadratic penalty item, which overcomes the shortcomings of traditional algorithms and suppresses the mode mixing between the structural vibration components. The validity and feasibility of the proposed method was verified by the simulation signal and was applied to a sluice prototype project. The results showed that the method effectively filtered noise, greatly improved the vibration response signal-to-noise ratio and obtained the structural vibration component time history signal, which provides a foundation for flood discharge structure vibration safety evaluation and health monitoring.

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