Automatic Detection of Lightning Whistlers Observed by the Plasma Wave Experiment Onboard the Arase Satellite Using the OpenCV Library

Ahmad, Umar Ali; Kasahara, Yoshiya; Matsuda, Shoya; Ozaki, Mitsunori; Goto, Yoshitaka

doi:10.3390/rs11151785

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…Dharma et al [24] used adaptive thresholding methods, a median filter, and an open method to remove noises in the spectrogram image recorded by the Akebono satellite, and then used the connected-component labeling method to label and detect lightning whistlers. Ahmad et al [25] adopted the Bresenham algorithm to automatically detect lightning whistlers recorded by the Arase satellite. Konan et al [26] developed a machine-learning-based model capable of automatically detecting whistlers using the Sliding Deep Neural Convolutional Neural Network (SDNN) and You Only Look Once Version 3 (YOLOV3) object detection network.…”

Section: Lightning Whistlersmentioning

confidence: 99%

“…The physical mechanism that produces this feature is that the higher-frequency electromagnetic wave propagates faster and arrives first [20]. In the time-frequency spectrum, the frequency gradually decreases with time, which is called the dispersion spectrum [24,25]. As shown in Figure 2, different colors represent the power spectral density of the electric field; f 1 is the frequency corresponding to the maximum power spectral density at time t 1 , and f 2 is the frequency corresponding to the maximum power spectral density at time t 2 .…”

Section: Observationmentioning

confidence: 99%

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Assimilation and Inversion of Ionospheric Electron Density Data Using Lightning Whistlers

Xiang

Liu

et al. 2023

Remote Sensing

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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.

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Section: Lightning Whistlersmentioning

confidence: 99%

Section: Observationmentioning

confidence: 99%

Assimilation and Inversion of Ionospheric Electron Density Data Using Lightning Whistlers

Xiang

Liu

et al. 2023

Remote Sensing

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“…Therefore, it is of great importance to develop a reliable method to automatically detect the tweek signals and then conduct statistical studies. Some methods exist that are dedicated to solving this problem, most of which, however, first process/filter the VLF data, and then pick out tweek signals based on the dispersive feature [31][32][33]. More recently, Zhou et al [34] proposed an automatic tweek detection model, based on the maximum entropy spectral estimation (MESE) method, by setting some thresholds for the morphological feature of tweek signals and achieved a detection accuracy of 77.4%.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of VLF Tweek Signals Based on the YOLO Model

Xu,

Ma,

Wang

et al. 2023

Remote Sensing

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Tweek signals are a special type of VLF (very low frequency) pulse, originally produced by lightning discharge, which becomes dispersive after repetitive bounces within the waveguide between the Earth’s surface and lower ionosphere. As such, tweek signals carry critical information about the region near the reflection height of the VLF waves, namely the D-region ionosphere. Although tweek measurements have been widely utilized in studies of the D-region ionosphere and lightning discharge, few statistical studies have been conducted, mainly due to the difficulty of manually identifying tweek signals from the enormous amount of VLF data with heavy noise. Considering the importance of tweek signals and the lack of a high-precision detection model, in this study, we propose a method to automatically and accurately pick out tweek signals from VLF measurements. This method is explicitly developed based on the you only look once (YOLO) model and a post-tracing process. Using a total of 2495 randomly selected VLF spectrogram images as the testing set, we evaluated the performance of this method. The precision and recall are found to be 92.0% and 89.2% for the first-order mode, and 97.5% and 86.7% for the first-two-order mode tweek, respectively. The time needed to process 10-s VLF measurements with a cadence of 4 μs is only 6.5 s, allowing for identifying the tweek signals from continuous VLF measurements in real time. Therefore, this method represents a reliable means to automatically detect tweek signals and enables the opportunity to statistically investigate the D-region ionosphere and lightning discharge via these signals.

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“…Ali Ahmad et al [13] processed the edges and lines via an image-processing technique to represent the features of the LW event and employed decision trees to classify it. Using the data observed by the Wuhan VLF ground network, Zhou et al [14] applied the clustering method, setting the energy-spectrum threshold and time-width threshold of the time-frequency graph to identify the lightning tweek waves. Yuan, et al [15] proposed an L-shape convolution kernel to enhance the features of the LW to obtain satisfactory classification results.…”

Section: Introductionmentioning

confidence: 99%

Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite

Li,

Yuan,

Cao

et al. 2023

Atmosphere

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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.

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Automatic Detection of Lightning Whistlers Observed by the Plasma Wave Experiment Onboard the Arase Satellite Using the OpenCV Library

Cited by 7 publications

References 23 publications

Assimilation and Inversion of Ionospheric Electron Density Data Using Lightning Whistlers

Assimilation and Inversion of Ionospheric Electron Density Data Using Lightning Whistlers

Automatic Detection of VLF Tweek Signals Based on the YOLO Model

Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite

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