Driven by the objective of earthquake disaster prevention and mitigation, China launched the Zhangheng mission to build a stereoscopic earthquake monitoring system from the lithosphere to space. This report briefly presents the possible seismic ionospheric disturbances recorded by the first probe of the Zhangheng mission, which is known as the China-Seismo-Electromagnetic Satellite (CSES). The routine data preprocessing and seismo-ionospheric information analysis methods are briefly introduced. The possible seismo-ionospheric disturbances that appeared during the strong shallow earthquakes (with a magnitude over 7 and a depth shallower than 30 km) are analyzed by using CSES and other multi-source data. Investigating seismo-ionospheric mechanisms requires multidisciplinary knowledge involving geophysics, atmosphere/ionosphere physics, geochemistry/atmospheric chemistry, etc. We state that the results from the CSES scientific application center are preliminary, calling for international scientists to contribute to the seismo-ionospheric perturbation phenomena, which is one of the most challenging scientific problems.

This study reports the temporal and spatial distributions of the extremely/very low frequency (ELF/VLF) wave activities and the energetic electron fluxes in the ionosphere during an intense storm (geomagnetic activity index Dst of approximately −174 nT) that occurred on 26 August 2018, based on the observations by a set of detectors onboard the China Seismo-Electromagnetic Satellite (CSES). A good correlation of the ionospheric ELF/VLF wave activities with energetic electron precipitations during the various storm evolution phases was revealed. The strongest ELF/VLF emissions at a broad frequency band extending up to 20 kHz occurred from the near-end main phase to the early recovery phase of the storm, while the wave activities mainly appeared at the frequency range below 6 kHz during other phases. Variations in the precipitating fluxes were also spotted in correspondence with changing geomagnetic activity, with the max values primarily appearing outside of the plasmapause during active conditions. The energetic electrons at energies below 1.5 MeV got strong enhancements during the whole storm time on both the day and night side. Examinations of the half-orbit data showed that under the quiet condition, the CSES was able to depict the outer/inner radiation belt as well as the slot region well, whereas under disturbed conditions, such regions became less sharply defined. The regions poleward from geomagnetic latitudes over 50° were found to host the most robust electron precipitation regardless of the quiet or active conditions, and in the equatorward regions below 30°, flux enhancements were mainly observed during storm time and only occasionally in quiet time. The nightside ionosphere also showed remarkable temporal variability along with the storm evolution process but with relatively weaker wave activities and similar level of fluxes enhancement compared to the ones in the dayside ionosphere. The ELF/VLF whistler-mode waves recorded by the CSES mainly included structure-less VLF waves, structured VLF quasi-periodic emissions, and structure-less ELF hiss waves. A wave vector analysis showed that during storm time, these ELF/VLF whistler-mode waves obliquely propagated, mostly likely from the radiation belt toward the Earth direction. We suggest that energetic electrons in the high latitude ionosphere are most likely transported from the outer radiation belt as a consequence of their interactions with ELF/VLF waves.

The theoretical development of Schumann Resonances has spanned more than a century as a form of global natural electromagnetic resonances. In recent years, with the development of electromagnetic detection technology and the improvement in digital processing capabilities, the connection between Schumann Resonances and natural phenomena, such as lightning, earthquakes, and Earth’s climate, has been experimentally and theoretically demonstrated. This article is a review of the relevant literature on Schumann Resonance observation experiments, theoretical research over the years, and a prospect based on space-based observations. We start with the theoretical background and the main content on Schumann Resonances. Then, observations and the identification of Schumann Resonance signals based on ground and satellite data are introduced. The research and related applications of Schumann Resonances signals are summarized in terms of lightning, earthquakes, and atmosphere. Finally, the paper presents a brief study of Schumann Resonances based on the China Seismo-Electromagnetic Satellite (CSES) and preliminary ideas about how to improve the identification and application of space-based Schumann Resonances signals.

In surface active seismic exploration, the near-surface imaging based on reflection signals provides valuable information for mineral exploration and deeper resources. However, the strong noises at near surface and stretching effects at nonzero offsets with conventional seismic data processing method severely damage the seismic reflection signals with low fold at near surface. The innovative method for the enhancement of seismic signals is the seismic interferometry method. However, due to the one-sided illumination, the seismic interferometry is still not suitable for the retrieval of reflection signals in most practical situations. In this article, we present the correlation-based interferometry (CI) method to enhance the reflection signals for near-surface imaging using the first cross correlation to construct virtual reflection signals and the second cross correlation to achieve the interferometric normal move-out (INMO) correction of the seismic reflection events with no stretching, which keeps the high resolution of near-surface reflection signals. Meanwhile, because of constructive interference stack, the CI method can significantly improve the signal-to-noise ratio (SNR) of reflection signals. The synthetic and field seismic data examples illustrate that the data-driven CI method performs better than the conventional stack (CS) method based on the velocity model for the near-surface reflection imaging.

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.