The predicting accuracy of geomagnetic field is a major factor influencing magnetic anomaly detection, geomagnetic navigation and geomagnetism. The limitations of current methods consist of complex model, a large number of parameters, method of solving parameters with high complexity and low forecast accuracy during geomagnetic disturbed days. In this paper we explore a deep learning method for forecasting geomagnetic field that adopts structure of recurrent neural networks (RNN) based on long-short term memory (LSTM). This method of LSTM RNN includes analyzing the characteristics of geomagnetic field and training the data set of geomagnetic data with simple and robust mathematical model. Compared with current methods, the high-precision prediction of geomagnetic field based on LSTM RNN is achieved during both geomagnetic quiet and disturbed days. Furthermore, it could be found that the average error and maximum error of LSTM RNN are far smaller than those of the other methods.


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    Title :

    Recurrent Neural Networks based on LSTM for Predicting Geomagnetic Field


    Contributors:
    Liu, Tong (author) / Wu, Tailin (author) / Wang, Meiling (author) / Fu, Mengyin (author) / Kang, Jiapeng (author) / Zhang, Haoyuan (author)


    Publication date :

    2018-09-01


    Size :

    169131 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English