A New Ionogram Automatic Scaling Method

Chen, Zi-Wei; Gong, Zongqiang; Zhang, Feng; Fang, Guangyou

doi:10.1029/2018rs006574

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…For real‐time monitoring of the ionosphere it is essential to achieve automatic scaling and inversion of ionograms with accuracy as high as possible. Chen ZW et al (2018) developed such a method to scale ionograms automatically, based on pattern recognition technology, mathematical morphology, and the echo characteristics of each layer of the ionosphere. The method has low computational complexity, strong universality, and high accuracy (over 90%) when it was used to scale observations of Chinese Academy of Sciences Digital Ionosonde (CAS‐DIS).…”

Section: Radio Wave Propagation In the Ionosphere And Sounding Technimentioning

confidence: 99%

Recent ionospheric investigations in China (2018–2019)

Liu

Wan

2020

Earth and Planetary Physics

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Key Points:Ionospheric observations have continuously accumulated with increasing GNSS receivers being installed q Studies of ionospheric climatology and space weather highlight new physical understanding q Study of ionospheric irregularities/scintillations reports interesting features q Abstract: Since the release of the 2018 National Report of China on ionospheric research ( Liu LB and Wan WX, 2018) to the Committee on Space Research (COSPAR), scientists from Mainland China have made many new fruitful investigations of various ionospheric-related issues. In this update report, we briefly introduce more than 130 recent reports ( [2018][2019]. The current report covers the following topics: ionospheric space weather, ionospheric structures and climatology, ionospheric dynamics and couplings, ionospheric irregularity and scintillation, modeling and data assimilation, and radio wave propagation in the ionosphere and sounding techniques. ΔV para (m/s) SYM-H (nT) V para (m/s) V para (m/s) ΔV para (m/s) Figure 2. (a) The SYM-H index on 13-17 July 2012. The blue line marks the storm onset (at 06:42 UT on 15 July); yellow and gray areas indicate the 24-hour storm-time and 24-hour quiet-time intervals. (b) The blue dots denote the storm-time equatorial field-aligned plasma drifts observed by C/NOFS with red curve representing median and red lines denoting upper and lower quartile values. (c) Same as panel (b), but for the 24-hour quiet time interval. (d) The difference between (b) and (c). (e-h) Same as panels (a-d), but for the case on 7-11 July 2012 where the 24-hour quiet and storm intervals start at 04:12 UT on 7 and 9 July, respectively, after Zhang R et al. (2018). Earth and Planetary Physics

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Section: Radio Wave Propagation In the Ionosphere And Sounding Technimentioning

confidence: 99%

Recent ionospheric investigations in China (2018–2019)

Liu

Wan

2020

Earth and Planetary Physics

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“…Several tools have been developed to extract important features from ionograms automatically, e.g., Autoscala (Scotto and Pezzopane, 2002;Pezzopane and Scotto, 2007), Univap Digital Ionosonde Data Analysis (UDIDA) (Pillat et al, 2013) and other methods, see for example,: Ding et al (2007); Jiang et al (2013Jiang et al ( , 2015; Chen et al (2018). In addition, automatic methods specialized for processing oblique ionograms have been developed, see for example,: (Ippolito et al, 2015;Song et al, 2016;Jiang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

NOIRE-Net–a convolutional neural network for automatic classification and scaling of high-latitude ionograms

Kvammen,

Vierinen,

Huyghebaert

et al. 2024

Front. Astron. Space Sci.

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Millions of ionograms are acquired annually to monitor the ionosphere. The accumulated data contain untapped information from a range of locations, multiple solar cycles, and various geomagnetic conditions. In this study, we propose the application of deep convolutional neural networks to automatically classify and scale high-latitude ionograms. A supervised approach is implemented and the networks are trained and tested using manually analyzed oblique ionograms acquired at a receiver station located in Skibotn, Norway. The classification routine categorizes the observations based on the presence or absence of E− and F-region traces, while the scaling procedure automatically defines the E− and F-region virtual distances and maximum plasma frequencies. Overall, we conclude that deep convolutional neural networks are suitable for automatic processing of ionograms, even under auroral conditions. The networks achieve an average classification accuracy of 93% ± 4% for the E-region and 86% ± 7% for the F-region. In addition, the networks obtain scientifically useful scaling parameters with median absolute deviation values of 118 kHz ±27 kHz for the E-region maximum frequency and 105 kHz ±37 kHz for the F-region maximum O-mode frequency. Predictions of the virtual distance for the E− and F-region yield median distance deviation values of 6.1 km ± 1.7 km and 8.3 km ± 2.3 km, respectively. The developed networks may facilitate EISCAT 3D and other instruments in Fennoscandia by automatic cataloging and scaling of salient ionospheric features. This data can be used to study both long-term ionospheric trends and more transient ionospheric features, such as traveling ionospheric disturbances.

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“…Classical manual scaling by professional researchers has been used, but several methods of automatic scaling have been proposed in recent decades. There are two main approaches to automatic scaling: image processing (computer vision) approaches [2], [3] and deep-learning image generation approaches [4], [5]. The both methods perform the scaling of ionograms with higher accuracy, but the expected ionogram for scaling is high signal-tonoise ratio (SNR) image.…”

Section: Introductionmentioning

confidence: 99%

A penalized motion detection model for extracting ionospheric echoes from low signal-to-noise ratio Ionogram video images

Hiroshige,

Fujimoto,

Ikeda

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

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Measuring the altitude distribution of electron density in the upper atmosphere, known as the ionosphere, using High-Frequency radio wave reflections often causes the low signal-to-noise ratio of ionospheric echoes due to radio frequency interference. We propose a model for converting low-signal-to-noise-ratio ionospheric echo video images (Ionogram) into noise-reduced images using image processing techniques, for tracing the ionospheric echoes from Ionogram. The proposed method consists of three processing parts: noise removal optimized for individual Ionogram images, extraction of ionospheric echoes by penalized background subtraction technique, and fine-tuning of ionospheric echo signals using a minimum spanning tree algorithm. The proposed model successfully reproduces fine Ionograms with 98% recall and 99% precision.

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A New Ionogram Automatic Scaling Method

Cited by 13 publications

References 15 publications

Recent ionospheric investigations in China (2018–2019)

Recent ionospheric investigations in China (2018–2019)

NOIRE-Net–a convolutional neural network for automatic classification and scaling of high-latitude ionograms

A penalized motion detection model for extracting ionospheric echoes from low signal-to-noise ratio Ionogram video images

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