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A retrospective analysis of complex geophysical data around the time of the two most powerful earthquakes that occurred in Alaska and had magnitudes M = 8.2 (29 July 2021) and M = 9.2 (28 March 1964), respectively, is carried out. The aim of the research is to assess the maximum possible magnitude of the electric field of a seismogenic nature that penetrated the ionosphere/plasmasphere, which could cause the ionospheric effects observed experimentally. Theoretical calculations have shown that under the geophysical conditions that existed before these earthquakes (favorable for the penetration of the seismogenic field into the ionosphere), the maximum value of a quasi-static electric seismogenic field in the ionosphere, perpendicular to geomagnetic field lines (tens of hours/units of days before the earthquake) for earthquakes with magnitudes M = 8–9 could reach 1–2 mV/m. Such values are sufficient for the formation of a plasmaspheric ULF-ELF-VLF-duct, which is formed in the vicinity of the geomagnetic field-line passing through the epicenter of the earthquake under the influence of a seismogenic electric field that penetrated into the ionosphere/plasmasphere. This leads to an anomalous amplification of the captured ULF-ELF-VLF waves, ULF (DC-16 Hz), ELF (6 Hz–2.2 kHz), VLF (1.8–20 kHz), not only above the epicenter of the future earthquake, but also at the point magnetically conjugated with the epicenter of the earthquake, testifying to the formation of such a duct, stretched along the geomagnetic field from one hemisphere to another, and formed on closed L-shells shortly before the earthquake. This result is confirmed by the measurements of the mission of the CSES satellite (China-Seismo-Electromagnetic Satellite) for the 29 July 2021 earthquake with magnitude M = 8.2.

Since the CSES (China Seismo-Electromagnetic Satellite) has been in orbit, it has detected a large number of constant-frequency electromagnetic disturbances (CFEDs), which are horizontal lines on the spectrum. In this paper, we present an algorithm for automatic recognition of CFEDs based on computer vision technology. The relevant results are of great significance for analysis of perturbation events and mining of the transformation laws of global space events. First, a grayscale spectrogram is obtained; then, a horizontal convolution kernel is used to enhance the horizontal edge features of the grayscale graph, and finally, black-and-white binarization is performed to complete data preprocessing. The preprocessed data are then fed into an unsupervised cluster model for training and recognition to realize automatic recognition of CFEDs. Experimental results show that the CFED recognition algorithm proposed in this paper is effective, with a recognition accuracy of more than 98%.

The recognition algorithm of the lightning whistler wave, based on intelligent speech, is the key technology to break the bottleneck of massive data and study the temporal and spatial variation rules of the lightning whistler wave. However, its recognition effect depends on the hyperparameters determined by manual experiments repeatedly, which takes a great deal of time and cannot guarantee the best recognition effect of the model. Therefore, we proposed the lightning whistler wave recognition algorithm based on grey wolf optimization (GWO). In this paper, the GWO algorithm is used to automatically find the best value of hyperparameters of Long Short-Term Memory (LSTM) in their limited searching space. Here we consider the number of hidden units (hu) and learning rate (lr) as the hyperparameters to be optimized, and the spatial coordinate (hu, lr) as the grey wolf position. By the end of the GWO process, we obtain the position of the wolf king α with the optimal hu and lr searched by the GWO algorithm. Then we use the optimal hu and lr to configure LSTM and perform supervised learning on the train set to obtain the final lightning whistler wave speech recognition model. Through experimental verification, the recognition model based on the GWO not only overcomes the uncertainty of the traditional model relying on manual finetuning of parameters and realizes the mechanism of automatic search and acquisition of hyperparameters, but also its recognition effect improves by about 2% in accuracy, F1score, and other metrics compared with the model trained by manually setting hyperparameters.