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The equatorial post-midnight irregularities, which has been less studied, are investigated using the in situ electron density (Ne) measurements from 2019 to 2021 obtained by the China Seismo-Electromagnetic Satellite (CSES) orbiting in the topside ionosphere. Some results are obtained as following. (1) The equatorial post-midnight irregularities are distributed along the dip equator with a longitude variation pattern of wavenumber 4 and seasonal variation pattern of summer-winter peaks during low solar activity (LSA) period in the topside ionosphere. (2) The longitude variation pattern of post-midnight irregularities is consistent with that of the background Ne with the irregularities concentrated on the peaks of background Ne. (3) The occurrence rate of post-midnight irregularities increases rapidly with increasing solar flux, which can be attributed to the rapid increase in the background Ne during LSA period; moreover, growth rate of occurrence rate is different across different longitudes, with the Pacific and the Atlantic longitudes being the most outstanding regions. (4) The similar characteristics for both post-midnight and post-sunset irregularities suggests they may have similar generation mechanisms, which are supported by the fact that conditions favorable to post-sunset irregularity generation can appear at midnight hours during LSA period. Since there have been far fewer post-midnight irregularity studies compared to post-sunset irregularity studies, much more work is still required to understand its generation mechanisms. The large number of post-midnight Ne measurements from the CSES satellite provides a good opportunity for this study, which can help to understand the post-midnight ionospheric dynamical processes and their variations with solar activity, as well as to develop a more comprehensive irregularity forecast model.

The nighttime winter anomaly (NWA) phenomenon is studied using the in situ electron density measurements from the CSES (China Seismo-Electronmagnetic Satellite) satellite orbiting above the F2 layer peak height (hmF2) region. Observations in the present study indicates that: (1) The NWA phenomenon can appear at mid-latitudes in large longitude ranges above the hmF2 region during low solar activity period, with the most obvious part being located at the American longitude sector during northern winter time and at the Asian longitude sector during the southern winter time as mention in previous studies. (2) The NWA phenomenon can also appear in the equatorial region. The equatorial NWA is located at the American to Eurasian longitude sectors during northern winter time and at the eastern Pacific longitude sector during southern winter time, and its occurrence and distribution vary with solar activity, which is similar to that of the mid-latitude NWA. (3) The formation mechanism for the mid-latitude NWA can be explained by the ionosphere-plasmasphere coupling process, and the formation mechanism for the equatorial NWA can be explained by the wind-driven plasma transporting process. These results are helpful for the in-depth understanding of the topside ionosphere, and the in situ electron density measurements from the CSES satellite provide us good opportunities to study the ionospheric climatological phenomena due to its special and unified observation local times.

The China Seismo-Electromagnetic Satellite (CSES) was successfully launched on February 2, 2018, with a plasma analyzer package (PAP) onboard to in-situ detect the ionospheric ion parameters. In this paper, the availability of PAP data is investigated for the first time through analyzing three typical ionospheric disturbance events. Firstly, the ionospheric radio and plasma disturbances caused by two high-power terrestrial very low frequency (VLF) transmitters over American NAA station and Australian NWC station are investigated. Furthermore, several possible anomaly observations before the 22 August 2018 Ms7.3 Venezuela earthquake are analyzed. In the southwest of the epicenter, the oxygen ion density increased significantly, and the ion drift velocity in vertical direction reversed synchronously from space-to-ground direction to ground-to-space direction. Finally, during a global intense geomagnetic storm event which started from August 25, 2018, the oxygen ion density well reflected the geomagnetic storm event’s temporal evolution process. The results show that the PAP onboard CSES has ability to discern the relative variations and features of ionospheric disturbances.

Discrimination of mirco-earthquake on source depth plays an important role in the field of micro-seismic monitoring. Conventional machine learning methods for data classification rely on carefully hand-engineered features that are vulnerable to low signal-to-noise ratio. Convolutional neural networks (CNNs) demonstrate some merits in dealing with structured data modelling where a set of meaningful features can be automatically extracted from sample learning. This paper explores the use of machine learning techniques for discrimination between deep and shallow mirco-seismic events. A benchmarked dataset including 444 micro-earthquakes from an underground cavern collapse in South Louisiana is employed for performance evaluation in this study, where several feature-based classifiers are compared against the CNN classifier. Empirical results show that the deep learning method outperforms the conventional classification techniques in discriminating the source depth.

The radio occultation (RO) measurements obtained by GOR payload on board the CSES (the first China Seismo-Electromagnetic Satellite) satellite are validated using the observations from five ionosonde stations located at different latitudes and one incoherent scatter radar (ISR) observatory located at mid-latitude. CSES RO peak values (NmF2) and peak heights (hmF2) are compared with the corresponding measurements from the five ionosonde stations, and RO electron density profiles (EDP) are compared with the corresponding ISR measurements. The results show that: (1) CSES RO NmF2 measurements are generally very consistent with that from ionosondes with a correlation coefficient of 0.9448. Obvious underestimation and overestimation are shown for the daytime NmF2 measurements obtained at and near EIA (Equatorial Ionization Anomaly) crest regions, and different error variation patterns are shown: the error at the EIA peak region increases with increase of the observations, while the error at the region near the EIA crest is almost a constant. Attention should be paid to these daytime measurements obtained at and near EIA region in applications. (2) CSES RO hmF2 measurements are in good agreement with that from ionosondes with a correlation coefficient of 0.8357. There is a tendency the daytime hmF2s are overestimated and the nighttime hmF2s are underestimated. (3) CSES RO EDP measurements generally show good consistency with that from ISR measurements. The RO measurements obtained within the peak height range (200–400 km) have the best performance, but errors increase out of this altitudes range. The error for the altitude of the E layer is relatively large, therefore RO measurements are less reliable for this altitude.

A preliminary validation of the in situ electron density data obtained by CSES, a newly launched electromagnetic satellite in China on Feb. 2, 2018, using the Swarm satellites LP measurements, is carried out in this study to assess the consistency and reliability of the LAP observations onboard CSES. Three ways are implemented to achieve the goal: (1) comparison of the global distribution patterns for both the daytime and nighttime data, (2) comparison of the data between CSES and Swarm at intersecting ground tracks with similar local time, and (3) comparison of the relative relationship between nighttime and daytime data using the nighttime enhancement space climatology phenomenon. The results show that the in situ electron densities estimated by LAP onboard CSES are in good agreement with data from the Swarm satellites in terms of global distributions, local details and relationship between nighttime and daytime data. A systematic bias between them is also found: LAP density estimates are much lower than that of Swarm satellites. However, the highly consistent relative variations between the two datasets proves the electron density dataset of LAP onboard CSES for now is applicable for the relative data variation applications on ionosphere-related studies. Further calibration of the LAP dataset is recommended using electron density data obtained by different measurements.

Feature selection of very high-resolution (VHR) images is a key prerequisite for supervised classification. However, it is always difficult to acquire the features which have the highest correlatio...

Lightning whistler waves, as an important tool for geospace exploration, can be found from the vast amount of electromagnetic satellite data. In recent years, with the development of computer vision and deep learning technologies, some advanced algorithms have been developed to automatically identify lightning whistler waves from the massive archived data of electromagnetic satellites. However, these algorithms fail to automatically extract the dispersion coefficients of lightning whistlers(DCW). Since the DCW are depended on the propagation path of lightning and geospace environments, it is extremely important for further geospace exploration.We proposed an algorithm that can automatically extract the dispersion coefficients of lightning whistler: (1) using two seconds time window on the SCM VLF data from the ZH-1 satellite to obtain segmented data; (2) generating time-frequency profile (TFP) of the segmented waveform by performing a band-pass filter and the short-time Fourier transform with a 94% overlap; (3) annotating the ground truth of the whistler with the rectangular boxes on the each time-frequency image to construct the training dataset; (4) building the YOLOV3 deep neural network and setting the training parameters; (5) inputting the training dataset to the YOLOV3 to train the whistler recognition model; (6) detecting the whistler from the unknown time-frequency image to extract the whistler area with the rectangle box as a sub-image; (7) conducting the BM3D algorithm to denoise the sub-image; (8) employing an adaptive threshold segmentation algorithm on the denoised sub-image to obtain the binary image which represents the whistler trace with the black pixel and other area with white pixel. (9) removing isolated points in the binary image with the open operation in morphology; (10) extracting lightning whistler trajectory region using connected domain analysis; (11) converting the trajectory coordinates from (t-f) to (f-0.5-t); (12) taking into account the Eckersley formula, which depicts the relationship between the scattering coefficient and the time frequency, we use the least-squares method on the converted trajectory coordinates to fit a straight line and obtain the slope of the line as the dispersion coefficient.In order to evaluate the effectiveness of the proposed algorithm, we construct two dataset: a simulation set and an observational dataset. The simulation set is composed of 1000 lightning whistler trajectories, which are generated according to the Eckersley formula. The observational dataset containing 1000 actual single-trace lightning whistler, are generated by collecting the data from the SCM VLF from the ZH-1 satellite. The experiment results show that: the mean-square error on the simulation set is below 2.8x 10-4; The mean-square error on the observational dataset is below 2.1054x10-3.Keywords: ZH-1 Satellite, SCM, Lightning Whistler, YOLOV3, Dispersion Coefficients

Lightning whistlers, found frequently in electromagnetic satellite observation, are the important tool to study electromagnetic environment of the earth space. With the increasing data from electromagnetic satellites, a considerable amount of time and human efforts are needed to detect lightning whistlers from these tremendous data. In recent years, algorithms for lightning whistlers automatic detection have been conducted. However, these methods can only work in the time-frequency profile (image) of the electromagnetic satellites data with two major limitations: vast storage memory for the time-frequency profile (image) and expensive computation for employing the methods to detect automatically the whistler from the time-frequency profile. These limitations hinder the methods work efficiently on ZH-1 satellite. To overcome the limitations and realize the real-time whistler detection automatically on board satellite, we propose a novel algorithm for detecting lightning whistler from the original observed data without transforming it to the time-frequency profile (image).The motivation is that the frequency of lightning whistler is in the audio frequency range. It encourages us to utilize the speech recognition techniques to recognize the whistler in the original data \of SCM VLF Boarded on ZH-1. Firstly, we averagely move a 0.16 seconds window on the original data to obtain the patch data as the audio clip. Secondly, we extract the Mel-frequency cepstral coefficients (MFCCs) of the patch data as a type of cepstral representation of the audio clip. Thirdly, the MFCCs are input to the Long Short-Term Memory (LSTM) recurrent neutral networks to classification. To evaluate the proposed method, we construct the dataset composed of 10000 segments of SCM wave data observed from ZH-1 satellite(5000 segments which involving whistler and 5000 segments without any whistler). The proposed method can achieve 84% accuracy, 87% in recall, 85.6% in F1score.Furthermore, it can save more than 126.7MB and 0.82 seconds compared to the method employing the YOLOv3 neutral network for detecting whistler on each time-frequency profile. Key words: ZH-1 satellite, SCM,lightning whistler, MFCC, LSTM

The power line harmonic generated by human activities can be found from the vast amount of the data observed by EFD on board the ZH-1 satellite. To study the human activities and remove the nonnegligible amount of interferences in the study of ionospheric precursors of earthquakes, we are desperate for finding the power line harmonic from the vast amount of data Hence, a novel automatic power line recognition method is proposed. Firstly, we utilize fourier transform on EFD data to obtain the power spectral density(PSD). Secondly, it is well known that harmonic radiation from power lines presents one or more horizontal linear characteristics on the PSD image and the color of the line is close to the color of the background in the image.In order to highlight the color contrast between the line and the background, we transform the PSD image from the RGB to the HSV color space and utilize the Saturation compoment of the HSV space as the object image.To obtain the edge regions, we process the object image with canny techniques. Finally, we use the Hough transform to detect the power line from the edge regions. To evaluate the proposed method, the experiment is performed for the dataset composed of 100 PSD images and each PSD image includes several interference lines. And the experimental result verifies the effectiveness of the proposed method with an accuracy of 86%.

The stratification phenomenon is investigated using the simultaneous in situ plasma density measurements obtained by the Swarm satellites orbiting at different altitudes above the F2 peak. For the first time, the continuous distribution morphology and the exact locations are obtained for the nighttime stratification, which show that the stratification events are centered at the EIA (equatorial ionization anomaly) trough and extend towards the two EIA crests, with the most significant part being located at the EIA trough. Another new discovery is the stratification in southern mid-latitudes; stratification events in this region are located on a local plasma peak sandwiched by two lower density strips covering all the longitudes. The formation mechanism of the stratification for the two latitudinal regions is discussed, but the stratification mechanism in southern mid-latitudes remains an unsolved problem. Highlights. This paper addresses the following: first application of in situ plasma densities for the direct analysis of the stratification in F2 layer, refined features of the exact location and continuous morphology for the stratification phenomenon, a new discovery of stratification covering all longitudes in southern mid-latitudes.

Stratification phenomenon is investigated using the simultaneous in situ plasma density measurements obtained by the Swarm satellites orbiting at different altitudes above F2 peak. For the first time, the continuous distribution morphology and the exact locations are obtained for the nighttime stratification, which show that the stratification events are centered at the EIA (equatorial ionization anomaly) trough and extend towards the two EIA crests with the most significant part being located at the EIA trough. Another new discovery is the stratification in southern mid-latitudes; stratification events in this region are located on a local plasma peak sandwiched by two lower density strips covering all the longitudes. The formation mechanism of the stratification for the two latitudinal regions is discussed, but the stratification mechanism in southern mid-latitudes remains an unsolved problem.

Many studies have revealed the stratification phenomenon of the topside ionospheric F2 layer using ground-based or satellite-based ionograms, which can show direct signs of this phenomenon. However, it is difficult to identify this phenomenon using the satellite-based in situ electron density data. Therefore, a statistical method, using the shuffle resampling skill, is adopted in this paper. For the first time, in situ electron density data, recorded by the same Langmuir probe aboard the DEMETER (Detection of Electro-Magnetic Emission Transmitted from Earthquake Regions) satellite at different altitudes, are analyzed, and a possible stratification phenomenon is identified using the proposed method. Our results show that the nighttime stratification, possibly a permanent phenomenon, can cover most longitudes near the geomagnetic equator, which is not found from the daytime data. The arch-like nighttime stratification decreases slowly on the summer hemisphere and thus extends a larger latitudinal distance from the geomagnetic equator. All results, obtained by the proposed method, indicate that the stratification phenomenon is more complex than what has previously been found. The proposed method is thus an effective one, which can also be used in similar studies of comparing fluctuated data.

CSES (China Seismo-Electromagnetic Satellite) is a newly launched electric-magnetic satellite in China. A GNSS occultation receiver (GOR) is installed on the satellite to retrieve electron density related parameters. In order to validate the radio occultation (RO) data from the GOR on board CSES, a comparison between CSES RO and the co-located COSMIC RO data is conducted to check the consistency and reliability of the CSES RO data using measurements from 12 February 2018 to 31 March 2019. CSES RO peak values (NmF2), peak heights (hmF2), and electron density profiles (EPDs) are compared with corresponding COSMIC measurements in this study. The results show that (1) NmF2 between CSES and COSMIC is in extremely good agreement, with a correlation coefficient of 0.9898. The near-zero bias between the two sets is 0.005363×105 cm−3 with a RMSE of 0.3638×105 cm−3, and the relative bias is 1.97 % with a relative RMSE of 16.17 %, which are in accordance with previous studies according to error propagation rules. (2) hmF2 between the two missions is also in very good agreement with a correlation coefficient of 0.9385; the mean difference between the two sets is 0.59 km with a RMSE of 12.28 km, which is within the error limits of previous studies. (3) Co-located EDPs between the two sets are generally in good agreement, but with a better agreement for data above 200 km than those below this altitude. Data at the peak height ranges show the best agreement, and then data above the peak regions; data below the peak regions, especially at the altitude of about the E layer, show relatively large fluctuations. It is concluded that CSES RO data are in good agreement with COSMIC measurements, and the CSES RO data are applicable for most ionosphere-related studies considering the wide acceptance and application of COSMIC RO measurements. However, particular attention should be paid to EDP data below peak regions in application as data at the bottom side of the profiles are less reliable than that at the peak and topside regions.

Many studies have revealed the stratification phenomenon of the topside ionospheric F2 layer using ground-based or satellite-based ionograms, which can show direct signs of this phenomenon. However, it is difficult to identify this phenomenon using the satellite-based in situ electron density data. Therefore, a statistical method, using the shuffle resampling skill, is adopted in this paper. For the first time, in situ electron density data, recorded by the same Langmuir probe onboard the Demeter satellite at different altitudes, are analyzed and a possible stratification phenomenon is identified using the proposed method. Our results show that the nighttime stratification, possibly a permanent phenomenon, can cover most longitudes near the geomagnetic equator, which is not found from the daytime data. The arch-like nighttime stratification decreases slowly on the summer hemisphere and thus extends a larger latitudinal distance from the geomagnetic equator. All results, obtained by the proposed method, indicate that the stratification phenomenon is more complex than what has previously been found. The proposed method thus is an effective one, which can also be used on similar studies of comparing fluctuated data.