在贝叶斯卷积神经网络的基础上识别地下人工腔
Jigen Xia1,2, Ronghua Peng1, Zhiqiang Li2
1School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
检测地下人造洞穴对于城市发展至关重要. 这项研究引入了表面电阻成像和贝叶斯卷积神经网络 (BCNN) 以改进腔体识别,提高地质物理调查的效率和准确性.
科学领域:
- 地质物理学 地质物理学
- 城市工程 城市工程
- 人工智能的人工智能
背景情况:
- 城市空间资源的利用依赖于地下的人工空洞.
- 这些空洞的数量和规模不断增加,需要先进的检测方法.
- 地质物理技术对于管理地下结构至关重要.
研究的目的:
- 介绍两种识别地下人造洞穴的方法.
- 为了提高洞穴检测的效率和准确性.
- 介绍一个新的贝叶斯卷积神经网络 (BCNN) 方法.
主要方法:
- 使用3D地球模型和实验验证的表面电阻成像.
- 基于贝叶斯卷积神经网络 (BCNN) 的快速识别方法的开发.
- 对BCNN与传统卷积神经网络进行比较分析.
主要成果:
- 表面电阻成像通过将模拟数据与已知的空洞位置进行比较,有效地识别地下空洞.
- 与传统方法相比,BCNN方法显著提高了分类的准确性和效率.
- BCNN在用于洞穴识别的表面电阻图像数据集上表现出卓越的性能.
结论:
- 开发的 BCNN 为地下人工空洞识别提供了高效和准确的解决方案.
- 表面电阻成像与BCNN相结合,提高了地球物理调查能力.
- 这些方法对于安全有效的地下工程和城市规划至关重要.
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