scholarly journals Changes in the ultra-low frequency wave field during the precursor phase to the Sichuan earthquake: DEMETER observations

2013 ◽  
Vol 31 (9) ◽  
pp. 1597-1603 ◽  
Author(s):  
S. N. Walker ◽  
V. Kadirkamanathan ◽  
O. A. Pokhotelov
Keyword(s):  
Low Frequency ◽  
Large Earthquake ◽  
Short Term ◽  
Frequency Wave ◽  
Sichuan Earthquake ◽  
Ulf Wave ◽  
Precursor Phase ◽  
Demeter Satellite ◽  
Short Term Prediction ◽  
Atmospheric Gravity

Abstract. Electromagnetic phenomena observed in association with increases in seismic activity have been studied for several decades. These phenomena are generated during the precursory phases of an earthquake as well as during the main event. Their occurrence during the precursory phases may be used in short-term prediction of a large earthquake. In this paper, we examine ultra-low frequency (ULF) electric field data from the DEMETER satellite during the period leading up to the Sichuan earthquake. It is shown that there is an increase in ULF wave activity observed as DEMETER passes in the vicinity of the earthquake epicentre. This increase is most obvious at lower frequencies. Examination of the ULF spectra shows the possible occurrence of geomagnetic pearl pulsations, resulting from the passage of atmospheric gravity waves generated in the vicinity of the earthquake epicentre.

scholarly journals A Short-Term Prediction Model of PM2.5 Concentration Based on Deep Learning and Mode Decomposition Methods

2021 ◽  
Vol 11 (15) ◽  
pp. 6915
Author(s):  
Jun Wei ◽  
Fan Yang ◽  
Xiao-Chen Ren ◽  
Silin Zou
Keyword(s):  
Neural Networks ◽  
Low Frequency ◽  
Decomposition Methods ◽  
Prediction Errors ◽  
Beijing City ◽  
Short Term ◽  
Mode Decomposition ◽  
Short Term Prediction ◽  
Bp Model ◽  
Pm2.5 Concentration

Based on a set of deep learning and mode decomposition methods, a short-term prediction model for PM2.5 concentration for Beijing city is established in this paper. An ensemble empirical mode decomposition (EEMD) algorithm is first used to decompose the original PM2.5 timeseries to several high- to low-frequency intrinsic mode functions (IMFs). Each IMF component is then trained and predicted by a combination of three neural networks: back propagation network (BP), long short-term memory network (LSTM), and a hybrid network of a convolutional neural network (CNN) + LSTM. The results showed that both BP and LSTM are able to fit the low-frequency IMFs very well, and the total prediction errors of the summation of all IMFs are remarkably reduced from 21 g/m3 in the single BP model to 4.8 g/m3 in the EEMD + BP model. Spatial information from 143 stations surrounding Beijing city is extracted by CNN, which is then used to train the CNN+LSTM. It is found that, under extreme weather conditions of PM2.5 <35 g/m3 and PM2.5 >150 g/m3, the prediction errors of the CNN + LSTM model are improved by ~30% compared to the single LSTM model. However, the prediction of the very high-frequency IMF mode (IMF-1) remains a challenge for all neural networks, which might be due to microphysical turbulences and chaotic processes that cannot be resolved by the above-mentioned neural networks based on variable–variable relationship.

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction
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