Singular spectrum analysis-based ionized layer anomaly detection method and system

Abstract

A singular spectrum analysis-based ionized layer anomaly detection method provided by the present invention comprises the steps of obtaining the historical ionized layer observation data ION; utilizing a singular spectrum analysis method to obtain an ionized layer normal variation component IONmain; calculating the normal background noise epsilon based on an ionized layer quiet period; according to the ionized layer normal variation component IONmain and the normal background noise epsilon, obtaining a normal variation range of the ionized layer observation data ION; when the actual ionized layer observation data exceeds the normal variation range of the ionized layer observation data ION, representing that an ionized layer is abnormal. According to the present invention, by the singular spectrum analysis, a periodic signal can be identified and reinforced, the extracted normal variation component contains the influences, such as the season change of the ionized layer along with the revolution of the earth, the 9-day, 13.5-day and 27-day periodic variation of the ionized layer caused by a sun 27-day rotation period, etc., so that the interferences caused by different external environments of the earth at a background field time period and the anomaly detection time period are removed from an anomaly detection result, and the ionized layer anomaly information can be detected effectively.

Description

technical field [0001] The invention belongs to the field of geophysics, and in particular relates to an ionospheric anomaly detection method and system based on singular spectrum analysis. Background technique [0002] The sun's extreme ultraviolet radiation and X-ray ionization, as well as the impact of solar wind and high-energy particle rays, have produced a large number of free electrons and ions in the upper atmosphere of the earth, forming an ionosphere at an altitude of 60-2000km. Ionospheric anomalies refer to special changes in which the physical characteristics of the ionosphere, such as electron density and peak height, deviate from its conventional form. Earthquakes, magnetic storms, tsunamis, volcanic eruptions and other factors may cause ionospheric anomalies. Determining the occurrence time of ionospheric anomalies is crucial for studies exploring the propagation process of magnetic storms in space weather variations, and the interaction between earthquakes ...

AI Technical Summary

Problems solved by technology

At present, the ionospheric anomaly detection methods in the world, such as time series method, quartile method, and second-order difference method, are all based on short-term ionospheric trend changes, without considering the regular periodic changes of the ionosphere, so the accuracy of anomaly detection not ideal

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