神经网络方法用于纠正LISST-VSF仪器中的多重散射错误
Optics express
|October 20, 2023
概括
一个新的神经网络纠正了体积散射函数 (VSF) 测量的错误,这是由于水中的多重散射引起的. 这种方法可以提高像LISST-VSF这样的仪器的精度,提高水下光学应用.
科学领域:
- 海洋光学 海洋光学
- 光学仪器仪器仪器仪器仪器仪器仪器仪器仪器仪器仪器仪器
- 数据科学数据科学数据科学
背景情况:
- LISST-VSF仪器测量体积散射函数 (VSF) 和自然水域的衰减,这对于遥感和水下通信至关重要.
- 由于多重散射,水中的高颗粒度会导致LISST-VSF测量的重大错误.
- 这些错误会影响衍生的光学特性,如散射系数和相位函数,从而限制仪器的可靠性.
研究的目的:
- 开发一种方法来纠正LISST-VSF测量的多重散射误差.
- 为了提高VSF的准确性,以及在的自然水域中获得的光学特性.
主要方法:
- 开发了一个feedforward神经网络来纠正VSF错误,仅使用测量VSF数据作为输入.
- 神经网络使用LISST-VSF的蒙特卡罗模拟在一系列散射系数 (0.05-50 m-1) 的范围内进行训练.
- 该模型使用自然水样本的VSF测量进行了验证.
主要成果:
- 神经网络准确地估计了VSF,与预期值密切匹配,在角形状和大小上没有多重散射误差.
- 对于预期分散系数为10.6m-1的自然水样,分散系数的误差从103%降至5%.
- 这种方法显著降低了VSF的不确定性,并在水中进行LISST-VSF测量的衍生性质.
结论:
- 神经网络方法有效地纠正LISST-VSF测量中的多重散射误差.
- 这种方法提高了光学测量的可靠性和准确性,在的水环境中.
- 改进的精度支持远程传感,环境监测和水下光通信的更广泛应用.
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