A5037038579- Ionosphere and magnetosphere dynamics
- Earthquake Detection and Analysis
- Geomagnetism and Paleomagnetism Studies
- GNSS positioning and interference
- Seismic Waves and Analysis
- Geophysics and Gravity Measurements
East China Jiaotong University
2022-2024
Ionospheric forecasts are critical for space-weather anomaly detection. Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel TEC forecasting model based on deep learning, which consists convolutional neural network (CNN), long-short term memory (LSTM) network, and attention mechanism. The mechanism added pooling layer fully connected assign...
Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, predict TEC by establishing short-term prediction model. We 15th-order spherical harmonic coefficients provided Center Orbit Determination in Europe (CODE) as training data set. Historical coefficient from 7 days,...
In this paper, we propose a convolutional gated recurrent unit (ConvGRU) deep learning method to forecast ionospheric total electron content (TEC) over China based on the regional maps (RIMs) from 2015 2018. Firstly, use GNSS observations Crustal Movement Observation Network of (CMONOC) generate RIMs (CRIMs). Secondly, CRIMs 2015-2017 as training set predict TEC in Finally, comparative experiments are carried out with ConvLSTM, International Reference ionosphere (IRI), and CODE's 1-day...
Monitoring and predicting ionospheric space weather is important for global navigation satellite system (GNSS) navigation, positioning, communication. Ionospheric total electron content (TEC) a vital indicator to measure weather. This study utilizes multichannel convolutional long short-term memory (ConvLSTM) with attention mechanism predict TEC maps considering the relevance of physical observations variations. The MConvLSTM-Attention method trained tested on regional (RIMs) three years...
Abstract The critical frequency of ionospheric F2 layer (foF2) is an important characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism implemented for predicting the foF2 inputs models are globally available ionosonde stations, geographic longitude latitude, world time (UT), geomagnetic activity index, solar index from 2015 to 2017. superiority analyzed different latitudes, seasons, conditions. results show...
Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction vital precise applications of Interferometric Synthetic Aperture Radar (InSAR) Global Navigation Satellite System (GNSS). This study proposes novel method ionospheric TEC that considers multiple TEC-related factors. We present random forest Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict global by incorporating...