基于深度学习下地磁干扰数据集的建立

Establishment of Geomagnetic Interference Data Set based on Deep Learning

针对应用于地磁干扰分类数据集匮乏的问题,通过对地磁干扰数据进行数据清理和标签分类等处理构建数据集,并使用主流深度学习算法对其进行训练与测试。实验结果表明,基于深度学习框架下,地磁干扰数据与未受干扰数据能实现较高准确率的分类结果,产出拥有一定效果的分类模型;其分类效果与数据样本集的建立有关,大量且干净的数据样本集将获得更好的分类结果及更细致的分类能力。

In view of the lack of data sets applied to geomagnetic interference classification,the data sets are constructed by processing the geomagnetic interference data,such as data cleaning and label classification,and trained and tested by using the mainstream deep learning algorithm.The experimental results show that under the framework of deep learning,the geomagnetic interference data and undisturbed data can achieve high accuracy classification results,and produce a classification model with certain effect;And its classification effect is related to the establishment of data sample set.A larger and cleaner data sample set will obtain better classification results and more detailed classification ability.