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Stacking data analysis method for Langmuir multi-probe payload

delete2025-08-17
delete0
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OA
AI
J
Jin Wang *
张
张端 (Duan Zhang)
张
张庆贺 (Qinghe Zhang) *
X
Xinyao Xie
F
Fangye Zou
Q
Qingfu Du
V
V. Manu
Y
Yan-Ju Sun
DOI：10.3389/fspas.2025.1614225delete
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摘要

摘要

En 中文
电离层中存在大量小尺度的电子密度不规则体。多针状朗缪尔探针（m-NLPs）的协同工作能够实现高空间分辨率的电子密度原位测量。然而，基于轨道运动限制（OML）理论的理论分析方法无法准确估计电子密度，即使在更高分辨率下，也受到卫星测量限制的影响。此外，由于卫星充电和飞行尾迹的影响，多探针之间的收集电流一致性较低，导致测量结果引入显著误差。本研究采用堆叠算法处理m-NLP数据，并引入国际参考电离层（IRI）模型校正预测的电子密度（Ne）值。堆叠模型的集成特性充分利用了多层感知器（MLP）、支持向量回归（SVR）、K近邻（KNN）和轻量梯度提升机（LightGBM）等模型的优点。集成机器学习方法与IRI模型的结合显著提高了m-NLP获取的电子密度测量精度。结果表明，即使多探针收集的电流一致性较差，采用此方法的预测结果的判定系数（R2）仍可达到0.9553，较传统诊断方法提高了0.5079。
Keyword:
ionosphere
electron density
needle Langmuir probes
machine learning
IRI model

期刊

F
Frontiers in Astronomy and Space Sciences
IF:
2.6
论文数:
420
被引数:
3.7K

机构

N
national space science center
学者数:
288
论文数: 100
被引数: 0
S
shandong university
学者数:
9.5W
论文数: 6.4W
被引数: 94
引用论文

引用论文

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