Generative Adversarial Networks for the Synthesis of Ionospheric Radio Sounding Ionograms: Challenges and Preliminary Results
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Abstract
The process of applying Generative Adversarial Networks (GANs) for the synthesis of ionospheric sounding ionograms is considered. This task is relevant in the context of constructing a statistical model of the ionosphere through empirical search. This paper describes the specifics of oblique ionospheric sounding as a subject area, particularly outlining the origin of the data and its physical meaning. The dataset, which includes radio sounding session configurations, environmental parameters, and ionograms, is presented and characterized. A method for converting and preprocessing raw data to form a training corpus is described.
The paper presents the general organization of conditional GANs used in image generation tasks, discusses key modifications to their structure specifically for ionogram synthesis, and provides a schematic demonstration of the architecture. Furthermore, the specifics of loss function selection, based on knowledge of the physical meaning of ionograms, are identified. Based on these considerations, a composite loss function is formulated, combining the discriminator's response with heuristics. Finally, the work provides examples of the results obtained, formulates key existing implementation challenges, and proposes potential solutions.
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References
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