An Automatic CADI’s Ionogram Scaling Software Tool for Large Ionograms Data Analytics

Abstract

Scale the ionosonde ionograms to produce accurate readings is a professional manual scaling technique. However, there is a high demand for auto-scaling software that can manage a large number of ionograms in order to avoid the time and effort involved in manual scaling as well as human errors. Noise-free, accurate trace identification and precise segmentation are required for the auto-scaling program to work. The Canadian Advanced Digital Ionosonde (CADI) ionograms are processed and auto-scaled using a new model on an open-source (Python) platform in this paper. Filtering the noise, Convolution Neural Network (CNN) based trace detection, layer-wise segmentation, and then extracting the ionospheric features are used to accomplish the scaling accuracy. The investigation uses raw ionogram files generated by the CADI system in Hyderabad, India (Lat: $17.47^{\circ }\text{N}$ , Long: $78.57^{\circ }\text{E}$ ) between 2014 and 2015. Raw ionograms in $^\ast $ .md4 or $^\ast $ .md2 file formats can be accepted by the suggested model (Individual or Hourly integrated). The proposed auto-scaling software tool’s individual block performance is examined with several classes of ionograms, and the overall performance is evaluated with a huge set of ionograms obtained during adverse space weather circumstances (16th to 18th March 2015). Univap Digital Ionosonde Data Analysis (UDIDA) software tool was considered for manual scaling. The results of manual scaling are compared with that of proposed scaling software. In fmin and h’f, respectively, the proposed model has a mean absolute error (MAE) of 0.36 MHz and 11.72 km, and a root mean square error (RMSE) of 0.7 MHz and 22.36 km.

Keywords

• Ionosonde
• CNN
• VGG-16
• auto-scaling