在地质勘探中,数字浮标表征技术和深度学习算法的协同应用
Zhicheng Dong1,2, Pan Tang3,4, Gongyang Chen1,2
1Research Institute of Mud Logging Technology and Engineering, Yangtze University, Jingzhou, 434023, China.
Scientific reports
|October 3, 2024
概括
这项研究为地质学家引入了一个数字外可视化平台,增强了现场数据分析. 该系统将3D建模与人工智能集成,用于准确的石质识别,提高地质勘探效率.
科学领域:
- 地质科学 地质科学
- 计算机科学 计算机科学
- 地质工程是地质工程.
背景情况:
- 传统的地质浮标分析在可视化,跨平台兼容性和易用性方面面临局限性.
- 地质学家需要先进的工具来有效和全面地解释现场数据.
研究的目的:
- 为增强地质数据分析开发一个新的现场数字浮标可视化平台.
- 提高外露形特征和石学识别的效率和准确性.
主要方法:
- 使用Cesium技术开发基于WebGL的3D数字外可视化平台.
- 通过无人机 (UAV) 来获取数据,用于3D地质模型创建倾斜摄影.
- 整合了改进的VGG19深卷积神经网络,用于石质识别.
主要成果:
- 该平台提供直观的3D地质模型与测量和绘图工具,改进数据理解.
- VGG19算法在识别细纹理和复杂岩石结构方面表现出高准确度.
- 案例研究证实了和VGG19集成在地质勘探中的可靠性.
结论:
- 和VGG19的协同应用显著提高了田间外露特征的效率和容易性.
- 这项技术为地质学家提供了更快,更全面的地质条件理解.
- 该研究通过先进的数字工具为地球科学研究开辟了新的途径.
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