Jigen Xia
Jigen Xia has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Xingmeng Dong
China Research Institute of Radio Wave Propagation
2 shared publications - Cheng Zhai
School of Safety Engineering, China University of Mining and Technology, Xuzhou, China
1 shared publication - Hexiang Xu
School of Safety Engineering, China University of Mining and Technology, Xuzhou, 221116, China
1 shared publication - Hongyang Xu
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
1 shared publication Jian Wu
China Research Institute of Radio Wave Propagation, Xinxiang, China
1 shared publication- Ting Huang
Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Yangtze University, Wuhan 430100, China
1 shared publication
Latest Publications
- 21 January 2024
- Published by Elsevier in Geoenergy Science and Engineering
- Vol. 234, 212675
Imaging small geological structure in coal mines based on reflected acoustic logging of underground cross-seam boreholes
- 21 January 2024
- Published by Elsevier in Geoenergy Science and Engineering
- Vol. 234, 212675
Imaging small geological structure in coal mines based on reflected acoustic logging of underground cross-seam boreholes
- Open Access
- 12 December 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 10, 129163-129175
Application of Semi-Airborne Frequency Domain Electromagnetic Data Based on Improved Ant-Colony-Optimized Wavelet Threshold Denoising Method
The semi-airborne frequency domain electromagnetic detection method is a popular electromagnetic detection method in which the transmission system is excited on the ground and the reception system coil is in the air to measure the vertical magnetic field signals in the frequency domain. In the field exploration, the semi-airborne frequency domain electromagnetic detection system is vulnerable to low-frequency motion noise, power-line interference, industrial noise, astronomical noise, and many other interferences, which leads to a very low signal-to-noise ratio of the detection data. Owing to the overlap between the effective signal and several types of noise in the time-frequency domain in the semi-airborne frequency domain of the detected signal, the conventional denoising methods are limited and cannot meet practical needs. Therefore, an integrated denoising method based on the improved ant-colony-optimized wavelet threshold is proposed. The method first separates the low-frequency noise of motion using the wavelet’s high-scale component for noise reduction. Other interference noise types, such as power-line interference, industrial noise and astronomical noise, are also reduced using the improved ant-colony-optimized wavelet threshold method. The experimental results show that the proposed integrated denoising method exhibits good suppression effects on several types of noise, can effectively improve the signal-to-noise ratio of the data, and improve the field data inversion accuracy.
- 12 December 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 10, 129163-129175
Open AccessApplication of Semi-Airborne Frequency Domain Electromagnetic Data Based on Improved Ant-Colony-Optimized Wavelet Threshold Denoising Method
The semi-airborne frequency domain electromagnetic detection method is a popular electromagnetic detection method in which the transmission system is excited on the ground and the reception system coil is in the air to measure the vertical magnetic field signals in the frequency domain. In the field exploration, the semi-airborne frequency domain electromagnetic detection system is vulnerable to low-frequency motion noise, power-line interference, industrial noise, astronomical noise, and many other interferences, which leads to a very low signal-to-noise ratio of the detection data. Owing to the overlap between the effective signal and several types of noise in the time-frequency domain in the semi-airborne frequency domain of the detected signal, the conventional denoising methods are limited and cannot meet practical needs. Therefore, an integrated denoising method based on the improved ant-colony-optimized wavelet threshold is proposed. The method first separates the low-frequency noise of motion using the wavelet’s high-scale component for noise reduction. Other interference noise types, such as power-line interference, industrial noise and astronomical noise, are also reduced using the improved ant-colony-optimized wavelet threshold method. The experimental results show that the proposed integrated denoising method exhibits good suppression effects on several types of noise, can effectively improve the signal-to-noise ratio of the data, and improve the field data inversion accuracy.