基于深度学习神经网络的地磁暴预报方法,利用乌拉干渺子霍德观测矩阵的时间序列

IF 0.7 4区 地球科学 Q4 GEOCHEMISTRY & GEOPHYSICS
V. G. Getmanov, A. D. Gvishiani, A. A. Soloviev, K. S. Zaitsev, M. E. Dunaev, E. V. Yekhlakov
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引用次数: 0

摘要

开发了一种基于深度学习神经网络的地磁暴预报方法,利用数字时间序列处理URAGANμ介子示波器的矩阵观测数据和标量Dst指数。提出了矩阵观测的计算操作方案和外推公式。选择了神经网络软件模块的变体及其参数。形成了预测和评估地磁暴正确和错误预测概率的决策规则。对地磁暴概率特征和预报间隔估计的实验研究证实了该方法的效率。所获得的预报结果将用于解决一些日地物理学和国民经济问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Method for Forecasting Geomagnetic Storms Based on Deep Learning Neural Networks Using Time Series of Matrix Observations of the Uragan Muon Hodoscope

A Method for Forecasting Geomagnetic Storms Based on Deep Learning Neural Networks Using Time Series of Matrix Observations of the Uragan Muon Hodoscope

A method for forecasting geomagnetic storms based on deep learning neural networks using digital time series processing for matrix observations of the URAGAN muon hodoscope and scalar Dst-indices has been developed. A scheme of computational operations and extrapolation formulas for matrix observations are proposed. The a variant of the neural network software module and its parameters are chosen. A decision-making rule is formed to forecast and assess the probabilities of correct and false forecasts of geomagnetic storms. An experimental study of estimates of the probabilistic characteristics and forecasting intervals of geomagnetic storms has confirmed the efficiency of the method. The obtained forecasting results are oriented towards solving a number of solar–terrestrial physics and national economic problems.

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来源期刊
Geomagnetism and Aeronomy
Geomagnetism and Aeronomy Earth and Planetary Sciences-Space and Planetary Science
CiteScore
1.30
自引率
33.30%
发文量
65
审稿时长
4-8 weeks
期刊介绍: Geomagnetism and Aeronomy is a bimonthly periodical that covers the fields of interplanetary space; geoeffective solar events; the magnetosphere; the ionosphere; the upper and middle atmosphere; the action of solar variability and activity on atmospheric parameters and climate; the main magnetic field and its secular variations, excursion, and inversion; and other related topics.
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