使用变压器神经网络的无人机的空磁补偿
Weiming Dai1,2, Changcheng Yang3, Shuai Zhou3
1College of Information Engineering, Gan Dong University, Fuzhou 344000, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
无人驾驶飞行器 (UAV) 的气磁调查面临发动机噪声干扰. 与传统方法相比,新的变压器神经网络算法显著提高了磁数据补偿的准确性.
科学领域:
- 地质物理学 地质物理学
- 地质物理勘探地质物理勘探
- 航空磁探测测量 航空磁探测测量
背景情况:
- 使用无人驾驶飞行器 (UAV) 的气磁测量对于各种领域的地下勘探至关重要.
- 飞机系统 (发动机,电子,金属结构) 引入磁噪声,影响数据准确性.
- 现有的方法使用数学模型和校准来消除干扰,但时间依赖性仍然是一个挑战.
研究的目的:
- 研究和改进用于无人机调查的气磁数据处理.
- 开发和评估一种用于对飞机引起的磁干扰进行补偿的新方法.
- 为了比较变压器神经网络与多层感知器网络的性能,用于气磁补偿.
主要方法:
- 使用托尔斯-劳森 (T-L) 模型进行无人机空气磁性调查的磁性干扰的数值模拟.
- 开发和应用变压器神经网络算法,以解决气磁数据中的时间依赖.
- 使用模拟和真实飞行数据对变压器网络与经典多层感知器 (MLP) 神经网络进行比较分析.
主要成果:
- 变压器神经网络在处理空气磁性数据方面表现出卓越的适配能力.
- 拟议的方法在补偿飞机引起的磁干扰方面取得了更高的准确性.
- 性能评估显示,变压器网络在此应用中比MLP具有显著的优势.
结论:
- 变压器神经网络在无人机调查中为气磁补偿提供了更有效的方法.
- 开发的算法提高了气磁数据的精度,导致更可靠的地下地质解释.
- 这一进步提高了使用无人机进行地球物理勘探的效率和准确性.
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