第一作者(共同一作†)期刊论文 |
[1]Xu, Z*., Lin X., and Li J. 2025. Adaptive Focusing Gravity Inversion based on Multi-Evaluation-Driven Mechanism, Geophysics [2]Qu, Z. †, Min, G. *, Xu, Z†, Xian, M., Zhang Y., She, A., Li, J., 2025. A Novel Depth-Weighting Approach Based on Regularized Downward Continuation for Enhanced Gravity Inversion. Remote Sensing, 17(7), pp.1184. [3]Xu, Z., Wang R*, Michael S. Z., Wang X., Li J., Zhang B., Liang S., Wang Y., 2023. Inversion of the Gravity Gradiometry Data by ResUet Network: An Application in Nordkapp Basin, Barents Sea. IEEE Transactions on Geoscience and Remote Sensing. [4]Liang, S.†, Wang X.*, Xu, Z†, Dai Y., Wang, Y., Guo, J., Jiao Y., Li, F., 2023. Steep subducting of Indian continental mantle lithosphere beneath the eastern Himalaya revealed by gravity anomalies. Science China Earth Sciences. [5]梁生贤†,王绪本*,徐铮伟†,代堰锫,王永华,郭镜,焦彦杰,李富. 2023. 重力异常揭示印度大陆地幔岩石圈在喜马拉雅东部之下陡峭俯冲. 中国科学. [6]Xu, Z., Zou, G.*, Wei, Q., Tian, J. and Yuan, H., 2021. Focusing joint inversion of gravity and magnetic data using a clustering stabilizer in a space of weighted parameters. Geophysical Journal International, 224(2), pp.1344-1359. [7]Xu, Z., Wang, R.*, Xiong, W., Wang, J. and Wang, D., 2021. 3D hybrid imaging based on gravity migration and regularized focusing inversion to predict the Poyang Basin interface. Geophysics, 86(4), pp. G55-G67. [8]Xu, Z*. and Zhdanov, M.S., 2015. Three-dimensional Cole-Cole model inversion of induced polarization data based on regularized conjugate gradient method. IEEE Geoscience and Remote Sensing Letters, 12(6), pp.1180-1184. [9]Xu, Z., Wan, L*. and Zhdanov, M.S., 2020. Focusing iterative migration of gravity gradiometry data acquired in the Nordkapp Basin, Barents Sea. Geophysical Prospecting, 68(7), pp.2292-2306. |
第一通讯作者 期刊论文 |
[1]Yu Zhang, Xu, Z.*, Minghao Xian, Michael S. Zhdanov, and Xuben Wang. 2026. Gravity Gradient Field Transformation by Enhanced Physics-Guided Multi-task Network. IEEE Transactions on Geoscience and Remote Sensing. [2]Minghao Xian, Xu, Z.*, Yu Zhang, Michael S. Zhdanov, et al. 2025. ResDM-Net++: An Enhanced Diffusion Model for Gravity Inversion, IEEE Transactions on Geoscience and Remote Sensing. [3]Liang, S., Xu, Z.*, Wang X., et al. 2024. Gravity Inversion in Spherical Coordinates with Dynamic Re-weighting Matrix, IEEE Transactions on Geoscience and Remote Sensing. [4]Zhang, Y., Xu, Z.*, Zhdanov, M., et al. 2024. 3D Basement Relief and Density Inversion Based on EfficientNetV2 Deep Learning Network, IEEE Transactions on Geoscience and Remote Sensing. [5]Xian, M., Xu, Z.*, Zhdanov, M., et al. 2024. Recovering 3D Salt Dome by Gravity Data Inversion Using ResU-Net++, Geophysics. [6]Wang, R., Ding, Y., Xu, Z.*, Zhdanov, M., et al. 2024. Employing MS-UNets Networks for Multiscale 3D Gravity Data Inversion: A Case Study in the Nordkapp Basin, Barents Sea, IEEE Transactions on Geoscience and Remote Sensing. [7]Wang, R, Xu, Z.*, Lai, C., Zhdanov, M., et al. 2024. Reconstructing 2-D Basement Relief Using Gravity Data by Deep Neuron Network: An Application on Poyang Basin, IEEE Transactions on Geoscience and Remote Sensing. [8]Li, J., Xu, Z*, Jian X., Li, M., Li, J., and Wang, X., 2023. Gravity and magnetic focusing inversion in revealing the metallogenic pattern of Dahongshan copper-iron deposit in the Kangdian area, China, IEEE Transactions on Geoscience and Remote Sensing. [9]Shang, T., Xu, Z.*, Gong, X., Li, X., Tian, S. and Guan, Y., 2021. Application of electrical sounding to determine the spatial distribution of groundwater quality in the coastal area of Jiangsu Province, China. Journal of Hydrology, 599, p.126348. [10]Wang, R., Zhao, H., Xu, Z.*, Ding, Y., Li G., Zhang, Y., Li, H., 2023. Real-time vehicle target detection in inclement weather conditions based on YOLOv4. Frontiers in Neurorobotics. [11] Wang, R., Wang, Z., Xu, Z*., Wang, C., Li, Q., Zhang, Y. and Li, H., 2021. A Real-Time Object Detector for Autonomous Vehicles Based on YOLOv4. Computational Intelligence and Neuroscience. [12] Zhang, B.; Yang, K.*; Cao, G.; Deng, J.; Xu, Z*; Yao, Y.; Chen, N.; Jiao, Y., 2024. The influence of different diagenesis on the elastic properties of different shale lithofacies: a case study of the upper Permian Wujiaping formation in East Sichuan Basin, China. Geomechanics and Geophysics for Geo-Energy and Geo-Resources. |
专利(国内外) |
[1]徐铮伟; 王绪本; 王睿; 王向鹏; 袁崑铭 ; 基于自适应多尺度深度学习网络预测地下密度的方法, 2023-01-19, 中国, 2023100555983(授权)。 [2]徐铮伟; 王绪本; 王睿; 梁生贤 ; 一种基于机器学习约束的密度突变界面反演方法及系统, 2022-08-23, 中国, CN202210583778.4(授权)。 [3]徐铮伟; 鲜明浩; 李军; 王建;梁生贤 ; 基于U-Net增强网络对地下密度快速成像方法及系统, 2023-09-23, 中国(授权)。 [4]鲜明浩;严震乾;黄悦瑶;税尹麒;徐铮伟;高波;基于改进的扩散模型的地质体重力数据反演方法及系统,2025-08-08,中国,ZL 2025 1 0473386.6(授权) [5]张宇;简楚;徐铮伟;眭超;基于U-net深度神经网络多任务学习的重力梯度数据位场转换方法及系统,2025-08-15,中国,ZL 2025 1 0704917.8(授权) [6]鲜明浩;张玉法; 徐铮伟; 一种应用于盆地基底界面起伏及密度的预测方法及系统,2024-07-05,中国,CN118011514B(授权) [7]张宇; 张玉法; 徐铮伟; 基于EfficientNetV2深度学习网络对三维盆地基底及密度反演, 2024-03-01, 中国(实审)。 [8]王绪本; 徐铮伟; 王向鹏; 罗耀华; 赵广东; 梁生贤 ; 一种基于深度学习网络快速预测二维盆地基底界面的方法, 2022-05-30, 中国, 202211587245X。 [9]Jun Li; Zhengwei Xu; Xuben Wang; Rui Wang; Shengxian Liang ; Density Abrupt Interface Inversion Method and System based on Machine Learning Constraints, 2023-3-3, 美国专利, 18/116,877(授权) |