Search Results (7)

Driven by the scientific objective of geophysical field detection and natural hazard monitoring from space, China launched an electromagnetic satellite, which is known as the China Seismo-Electromagnetic Satellite (CSES-01), on 2 February 2018, into a circular sun-synchronous orbit with an altitude of about 507 km in the ionosphere. The CSES-01 has been in orbit for over 6 years, successfully exceeding its designed 5-year lifespan, and will continually operate as long as possible. A second identical successor (CSES-02) will be launched in December 2024 in the same orbit space. The ionosphere is a highly dynamic and complicated system, and it is necessary to comprehensively understand the electromagnetic environment and the physical effects caused by various disturbance sources. The motivation of this report is to introduce the typical electromagnetic waves, mainly in the ELF/VLF band (i.e., ~100 Hz to 25 kHz), recorded by the CSES-01 in order to call the international community for deep research on EM wave activities and geophysical sphere coupling mechanisms. The wave spectral properties and the wave propagation parameters of those typical EM wave activities in the upper ionosphere are demonstrated in this study based on wave vector analysis using the singular value decomposition (SVD) method. The analysis shows that those typical and common natural EM waves in the upper ionosphere mainly include the ionospheric hiss and proton whistlers in the ELF band (below 1 kHz), the quasiperiodic (QP) emissions, magnetospheric line radiations (MLR), the falling-tone lightning whistlers, and V-shaped streaks in the ELF/VLF band (below 20 kHz). The typical artificial EM waves in the ELF/VLF band, such as power line harmonic radiation (PLHR) and radio waves in the VLF band, are also well recorded in the ionosphere. Full article

The electric field detector of the CSES satellite has captured a vast number of lightning whistler events. To recognize them effectively from the massive amount of electric field detector data, a recognition algorithm based on speech technology has attracted attention. However, this approach has failed to recognize the lightning whistler events which are contaminated by other low-frequency electromagnetic disturbances. To overcome this limitation, we apply the single-channel blind source separation method and audio recognition approach to develop a novel model, which consists of two stages. (1) The training stage: Firstly, we preprocess the electric field detector wave data into the audio fragment. Then, for each audio fragment, mel-frequency cepstral coefficients are extracted and input into the long short-term memory network for training the novel lightning whistler recognition model. (2) The inference stage: Firstly, we process each audio fragment with the single-channel blind source to generate two different sub-signals. Then, for each sub-signal, the mel-frequency cepstral coefficient features are extracted and input into the lightning whistler recognition model to recognize the lightning whistler. Finally, the two results above are processed by decision fusion to obtain the final recognition result. Experimental results based on the electric field detector data of the CSES satellite demonstrate the effectiveness of the algorithm. Compared with classical methods, the accuracy, recall, and F1-score of this algorithm can be increased by 17%, 62.2%, and 50%, respectively. However, the time cost only increases by 0.41 s. Full article

The data assimilation algorithm is a common algorithm in space weather research. In this paper, the time-frequency information in the dispersion spectrum of lightning whistlers received by the ZH-1 satellite is used as the observed value, and the international reference ionospheric model serves as the background model to construct the calculation model of the propagation time of lightning whistlers in the ionosphere. Kalman filtering is adopted to assimilate the electron density distribution along the propagation path of lightning whistlers. The results show that the situation where the electron density of the background model deviates greatly from the true value is significantly improved through data assimilation. The electron density after assimilation is in good agreement with the true value, which effectively helps realize the process of using observed values to correct the background value. On this basis, the influence of the frequency difference on the assimilation inversion effect is studied, and the results show that the assimilation effect is worse when the frequency difference between frequency points is less than 1 kHz. Full article

Reconstruction of reliable plasmaspheric electron density profiles is important for understanding physical processes in the plasmasphere. This paper proposes a technique that can be applied to correct the plasmaspheric electron density profiles using ray tracing by scrutinizing dispersion analyses of lightning whistlers. The Global Core Plasma Model and the International Reference Ionosphere were introduced as a reference electron density profile. Modifications of this electron density profile were then proposed to satisfy the dispersion characteristics of lightning whistlers measured by satellites in the plasmasphere. We first introduce two kinds of functions to modify the electron density: constant and linear, the linear function is more adequate. We applied our method to two lightning whistler events on 14 August 2017, measured by the Plasma Wave Experiment/Waveform Capture aboard the Arase satellite, and analyzed the dispersion of the observed lightning whistlers. We show how the density modification affects the delay time of the ray path and satisfies the dispersion characteristics under the appropriate adjustments. Full article

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. Full article

Whistler mode waves play a major role in regulating the lifetime of trapped electrons in the Earth’s radiation belts. Specifically, interaction with whistler mode hiss waves is one of the mechanisms that maintains the slot region between the inner and outer radiation belts. The generation mechanism of hiss is a topic still under debate with at least three prominent theories present in the literature. Lightning generated whistlers in their ducted or non-ducted modes are considered to be one of the possible sources of hiss. We present a study of new observations from the Radio Receiver Instrument (RRI) on the Enhanced Polar Outflow Probe (ePOP: also known as SWARM-E). RRI consists of two orthogonal dipole antennas, which enables polarization measurements, when the satellite boresight is parallel to the geomagnetic field. Here we present 105 ePOP - RRI events from 2014–2018, in which lightning whistlers(75) and hiss waves(39) were observed. In more than 50% of those whistler observations, hiss found to co-exist. Moreover, the whistler observations are correlated with observations of wave power at the lower-hybrid resonance. The observations and a whistler mode ray-tracing study suggest that multiple-hop lightning induced whistlers can be a source of hiss and plasma instabilities in the magnetosphere. Full article

The automatic detection of shapes or patterns represented by signals captured from spacecraft data is essential to revealing interesting phenomena. A signal processing approach is generally used to extract useful information from observation data. In this paper, we propose an image analysis approach to process image datasets produced via plasma wave observations by the Arase satellite. The dataset consists of 31,380 PNG files generated from the dynamic power spectra of magnetic wave field data gathered from a one-year observation period from March 2017 to March 2018. We implemented an automatic detection system using image analysis to classify the various types of lightning whistlers according to the Arase whistler map. We successfully detected a large number of whistler traces induced by lightning strikes and recorded their corresponding times and frequencies. The various shapes of the lightning whistlers indicate different very-low-frequency propagations and provide important clues concerning the geospace electron density profile. Full article