Ionospheric Electron Fluctuation Detection via Correlation Analysis
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Solution Overview
Problem
Detecting abnormal fluctuations in the number of electrons in the ionosphere before an earthquake with high accuracy is challenging due to natural variations and space weather interference.
Innovation Solution
An abnormality detection apparatus that calculates changes in the total number of electrons between observation stations and satellites, estimates future changes, calculates correlation values between stations, and determines abnormalities based on predetermined thresholds, allowing for accurate detection of ionospheric fluctuations before an earthquake.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If only data before earthquake occurrence is used for detection, then early earthquake prediction is enabled, but detection accuracy deteriorates due to natural variations and space weather
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and building statistical models of ionospheric electron fluctuations during normal periods (before earthquakes occur). The baseline information is prepared in advance, allowing the system to quickly compare post-earthquake data against pre-established patterns without requiring real-time post-event analysis, thus achieving both early detection and high accuracy
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing real-time electron fluctuation data against historical baselines and adjusting detection thresholds based on accumulated observations. The determination unit uses feedback from multiple data sources (natural variations, space weather conditions) to refine its abnormality detection criteria, improving accuracy while maintaining early detection capability
2Measurement precision
If multiple observation stations are used to improve detection reliability, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The system segments the observation network into multiple independent observation stations, each performing simple electron content measurements. By dividing the complex task of earthquake prediction into simpler sub-tasks performed at distributed locations, the system achieves high detection accuracy through data aggregation while keeping individual station complexity low. Each station independently calculates electron content changes, and the determination unit synthesizes these segmented results
Data Source
AI summary
A computer calculates a change amount of a total number of electrons from an observation start time in the ionosphere between an observation station and a satellite based on observation data of a signal received from the satellite by the observation station on the ground. The computer estimates the change amount of the total number of electrons to be calculated next based on the time change of the change amount of the total number of electrons from the observation start time in the ionosphere and calculates a difference (estimation error) between the estimated change amount of the total number of electrons and the actually calculated change amount of the total number of electrons. The computer calculates a correlation value between the estimation error calculated for each observation station and the estimation error calculated for a predetermined number of the observation stations in the vicinity of each observation station. In a case where the correlation value calculated for each observation station is a predetermined threshold value or more, when the correlation value is also the predetermined threshold value or more for the predetermined number of observation stations in the vicinity of the observation station, the computer determines that an abnormality has occurred in the ionosphere between the observation station and the satellite.