Statistical Analysis of Medium‐Scale Traveling Ionospheric Disturbances Over Japan Based on Deep Learning Instance Segmentation

Medium‐scale traveling ionospheric disturbances (MSTIDs) are observed as parallelly arrayed wavelike perturbations of Total Electron Content (TEC) in ionospheric F region leading to satellite navigation error and communication signal scintillation. The observation method for MSTIDs, detrended TEC (dTEC) map, summarizes the perturbation component of TEC having the merits of full‐time and two‐dimensional. However, previous automatic processing methods for dTEC map cannot discriminate MSTIDs from other irregular ionospheric perturbations intelligently. With the development of artificial intelligence in recent years, deep learning approach is expecting to clarify the controversy of MSTID external dependence (season and solar/geomagnetic activity) under debating for decades. Therefore, this research proposes a real‐time processing algorithm for dTEC maps based on Mask Region‐Convolutional Neural Network (R‐CNN) model of deep learning instance segmentation to detect wavelike perturbations intelligently with an accuracy of about 80% and a processing speed of about 8 fps. Then isolated perturbations are eliminated and only MSTID waveforms are chosen to obtain statistical characteristics of MSTIDs. With this algorithm, we analyzed up to 1,209,600 dTEC maps from 1997 to 2019 over Japan automatically and established a database of hourly averaged MSTID characteristics. This research introduces the partial correlation coefficient for the first time to clarify the solar/geomagnetic activity dependence of MSTID characteristics which is independent with each other. Plain Language Summary Medium‐scale traveling ionospheric disturbance (MSTID) is an ionospheric irregularity phenomenon observed as parallelly arrayed wavelike perturbations of Total Electron Content (TEC) with a period of less than 1 hr and wavelength of less than 500 km. The TEC is measured by the signal propagation delay between satellite and ground receiver network and its perturbation component is summarized in detrended TEC (dTEC) map as the observation method of MSTIDs. However, previous automatic processing methods for dTEC map cannot discriminate MSTIDs from other irregular ionospheric perturbations. The controversy of MSTID external dependence (season and solar/geomagnetic activity) has been under debating for decades in previous statistical analyses. To solve these problems, the first MSTID processing algorithm based on deep learning instance segmentation is proposed in this research to process up to