The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the tradit...The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.展开更多
随着城市化进程加速,智慧城市建设对建筑物三维信息的高效获取需求日益迫切。传统小区容积率测量方法依赖人工测量与二维图纸分析,存在效率低、成本高、动态更新困难等问题,难以满足现代城市精细化管理的需求。提出一种融合无人机三维...随着城市化进程加速,智慧城市建设对建筑物三维信息的高效获取需求日益迫切。传统小区容积率测量方法依赖人工测量与二维图纸分析,存在效率低、成本高、动态更新困难等问题,难以满足现代城市精细化管理的需求。提出一种融合无人机三维建模与深度学习技术的小区容积率快速计算方法:通过无人机倾斜摄影技术获取高分辨率影像数据,构建精确的三维模型以提取建筑物立体信息;结合改进YOLO(you only look once)算法对建筑立面窗户进行分层识别与定位,利用窗户分布特征与建筑高度的相关性推导容积率。该方法通过自动化流程替代传统人工操作,显著提高了数据采集与处理效率,降低了人力与时间成本,同时保障了计算结果的准确性和实时性。研究成果为城市规划与管理提供了高效的技术工具,推动城市资源分配优化与智慧化发展,为提升居民生活品质与城市可持续发展提供支撑。展开更多
基金supported by National Natural Science Foundation of China(42364008,41804110)in part by Guizhou Provincial Basic Research Program(Natural Science)(ZK[2022]060)+1 种基金in part by China Postdoctoral Science Foundation(2022M723127)in part by Youth Innovation Team Project of Shandong Provincial Education Department(2022KJ141).
文摘The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.
文摘随着城市化进程加速,智慧城市建设对建筑物三维信息的高效获取需求日益迫切。传统小区容积率测量方法依赖人工测量与二维图纸分析,存在效率低、成本高、动态更新困难等问题,难以满足现代城市精细化管理的需求。提出一种融合无人机三维建模与深度学习技术的小区容积率快速计算方法:通过无人机倾斜摄影技术获取高分辨率影像数据,构建精确的三维模型以提取建筑物立体信息;结合改进YOLO(you only look once)算法对建筑立面窗户进行分层识别与定位,利用窗户分布特征与建筑高度的相关性推导容积率。该方法通过自动化流程替代传统人工操作,显著提高了数据采集与处理效率,降低了人力与时间成本,同时保障了计算结果的准确性和实时性。研究成果为城市规划与管理提供了高效的技术工具,推动城市资源分配优化与智慧化发展,为提升居民生活品质与城市可持续发展提供支撑。
