Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method

Abstract

Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long distances. In this study, we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning method. The differences (automatic − manual) in each ionosphere parameter between the automatic method and the manual method were −0.07 ± 2.73 km, 0.03 ± 0.92 cm−3, and 91 ± 1,068 km for the ionospheric reflection height (h), equivalent electron densities at reflection heights (Ne), and propagation distance (d), respectively. Moreover, the automatic method is capable of recognizing higher harmonic tweek sferics. The evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.

Keywords

• machine learning method
• tweek atmospherics
• reflection height
• d-region ionosphere