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Your search keyword '"Mao, Lifeng"' showing total 14 results
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14 results on '"Mao, Lifeng"'

3. Research on Airborne Ground-Penetrating Radar Imaging Technology in Complex Terrain.

Author

Chi, Yuelong, Pang, Su, Mao, Lifeng, Zhou, Qiang, and Chi, Yuehua

Subjects
RADAR in aeronautics, RELIEF models, FINITE difference time domain method, DRONE aircraft, EXTREME environments
Abstract

The integration of ground-penetrating radar (GPR) with unmanned aerial vehicles (UAVs) enables efficient non-contact detection, performing exceptionally well in complex terrains and extreme environments. However, challenges in data processing and interpretation remain significant obstacles to fully utilizing this technology. To mitigate the effects of numerical dispersion, this paper develops a high-order finite-difference time-domain (FDTD(2,4)) three-dimensional code suitable for airborne GPR numerical simulations. The simulation results are compared with traditional FDTD methods, validating the accuracy of the proposed approach. Additionally, a Kirchhoff migration algorithm that considers the influence of the air layer is developed for airborne GPR. Different processing strategies are applied to flat and undulating terrain models, significantly improving the identification of shallowly buried targets. Particularly under undulating terrain conditions, the energy ratio method is introduced, effectively suppressing the interference of surface reflections caused by terrain variations. This innovative approach offers a new technical pathway for efficient GPR data processing in complex terrains. The study provides new insights and methods for the practical application of airborne GPR. [ABSTRACT FROM AUTHOR]

Published
2024
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7. 3-D Basement Relief and Density Inversion Based on EfficientNetV2 Deep Learning Network

Author

Zhang, Yu, Xu, Zhengwei, Xian, Minghao, Zhdanov, Michael S., Lai, Changjie, Wang, Rui, Mao, Lifeng, and Zhao, Guangdong

Abstract

Gravity interface inversion is a critical technique in delineating the substructure of basins, providing essential technological and data support for oil and gas exploration. Traditional gravity inversion approaches often encounter issues such as suboptimal local solutions and limited resolution. Moreover, conventional deep learning inversion methods typically require extensive time for empirical parameter adjustment, hindering the achievement of optimal training outcomes. By utilizing Bouguer gravity anomaly data, this research pioneers the application of the EfficientNetV2 network in predicting 3-D basement relief interfaces and variations in overburden density. The network employs a composite scaling technique to adaptively adjust its width, depth, and input resolution, thereby identifying the most effective network configuration. Concurrently, the innovative Fused-MBconv convolutional module efficiently achieves superior results with a reduced number of network parameters. Specifically, in the Poyang Lake Basin study in Jiangxi Province, China, the EfficientNetV2 model demonstrated enhanced accuracy in predicting density variations of the basement interface and overlying strata compared to traditional methodologies.

Published
2024
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