The Development of Spatial Attention U-Net for The Recovery of Ionospheric Measurements and The Extraction of Ionospheric Parameters

TL;DR

This paper presents a deep learning model, Spatial Attention U-Net, that effectively recovers and identifies ionospheric signals from noisy radar data, enabling precise measurement of ionospheric parameters.

Contribution

The study introduces a novel deep learning approach for ionogram signal recovery and parameter extraction, improving accuracy over traditional methods.

Findings

Accurately identifies F2 layer modes and E layer signals

Determines critical frequencies with high precision

Model performance depends on dataset size

Abstract

We train a deep learning artificial neural network model, Spatial Attention U-Net to recover useful ionospheric signals from noisy ionogram data measured by Hualien's Vertical Incidence Pulsed Ionospheric Radar. Our results show that the model can well identify F2 layer ordinary and extraordinary modes (F2o, F2x) and the combined signals of the E layer (ordinary and extraordinary modes and sporadic Es). The model is also capable of identifying some signals that were not labeled. The performance of the model can be significantly degraded by insufficient number of samples in the data set. From the recovered signals, we determine the critical frequencies of F2o and F2x and the intersection frequency between the two signals. The difference between the two critical frequencies is peaking at 0.63 MHz, with the uncertainty being 0.18 MHz.