一种基于卷积和长期短期记忆神经网络的空中重力梯度补偿方法
Shuai Zhou1, Changcheng Yang1, Yi Cheng1
1College of Geoexploration Science and Technology, Jilin University, Changchun 130012, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
这项研究引入了一种新的CNN-LSTM算法用于空中重力梯度测量,通过补偿飞机运动来显著提高准确性. 先进的方法提高了资源勘探的数据可靠性.
科学领域:
- 地质物理学 地质物理学
- 地球科学 地球科学 地球科学
- 航空航天工程 航空航天工程
背景情况:
- 空载重力梯度测量对于资源勘探至关重要,但对飞机运动敏感.
- 动态环境因素对测量精度有重大影响,需要先进的补偿技术.
研究的目的:
- 开发和评估用于空中重力梯度测量的后错误补偿算法.
- 提高从动态平台收集的重力检测数据的准确性和可靠性.
主要方法:
- 提出了一种使用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 神经网络 (CNN-LSTM) 的新型补偿算法.
- 利用CNN来提取特征,利用LSTM来捕获时间依赖关系的时间序列数据.
- 将算法应用于模拟和测量的空中重力梯度数据.
主要成果:
- 与传统的多层感知子 (MLP) 相比,CNN-LSTM模型在从结合的时间序列数据中学习方面表现出卓越的表现.
- 在空中重力梯度测量中实现了对补偿精度的显著改进.
- 在黑龙江省的现实数据上验证了拟议方法的有效性.
结论:
- CNN-LSTM算法提供了一个强大的解决方案,用于减轻空中重力梯度调查中运动诱导的错误.
- 这种先进的补偿技术提高了地球物理勘探的精度,特别是在资源和碳化合物检测方面.
- 该研究强调了深度学习在提高动态地球物理测量的准确性方面的潜力.
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