张凤凯

性别:男

所在单位:土建与水利学院

入职时间:2021-03-01

所属院系: 土建与水利学院

   
当前位置: 中文主页 >> 科学研究 >> 论文成果

Tunnel resistivity deep learning inversion method based on physics-driven and signal interpretability

发布时间:2024-10-06   点击数:

所属单位:土建与水利学院

论文名称:Tunnel resistivity deep learning inversion method based on physics-driven and signal interpretability

发表刊物:NEAR SURFACE GEOPHYSICS

第一作者:刘本超

论文编号:1770290024510185474

字数:50

是否译文:

发表时间:2024-02

上一条: Deep Neural Network-Based Permittivity Inversions for Ground Penetrating Radar Data

下一条: Arbitrarily-oriented tunnel lining defects detection from Ground Penetrating Radar images using deep Convolutional Neural networks