VAN method of short-term earthquake prediction shows promise

abstract The emission of gas from the Earth's crust is a complex process influenced by meteorological and seasonal parameters. The use of gas emission as a tool in earthquake prediction will require an understanding of these influences. Radon emanation has been integrated over weekly intervals and free mercury vapor emission over 212 hour intervals at a low seismic risk site in Colorado. Radon measured by the Track Etch® technique ranged from 136 to 1750 tracks/mm2 (81 to 1040 pC/liter) over the 1-yr period of the experiment. There was a strong correlation of radon emanation with: instrument vault temperature, barometric pressure, outside temperature, soil temperature, and whether or not the surface soil was frozen. Seasonal influences on radon emanation are important with 94 per cent of the variance being accounted for by the measured meteorological and seasonal parameters. Mercury concentrations in the instrument vault ranged from <1 to 53 ng/m3 over the 1 yr. Mercury emission correlates with vault temperature, vault relative humidity, outside temperature, barometric pressure, soil temperature and moisture, and the soil freeze-thaw cycle. Diurnal cycles are common but do not occur on all days. Other short-term noise in mercury emission is also important and phase shift or phase lag effects are important. Only 32 per cent of the variance in mercury emission can be accounted for by the measured meteorological and seasonal parameters. The short-term noise coupled with phase lags are important factors in mercury emission rates.

Download Full-text

Applied Research Of BP Neural Network In Earthquake Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.204-208.2449 ◽

2012 ◽

Vol 204-208 ◽

pp. 2449-2454 ◽

Cited By ~ 2

Author(s):

Wu Sheng Hu ◽

Hong Lin Nie ◽

Hao Wang

Keyword(s):

Neural Network ◽

Earthquake Prediction ◽

Bp Neural Network ◽

Integrated Model ◽

Earthquake Magnitude ◽

Linear Regression Method ◽

Network Technology ◽

Short Term ◽

Non Linear ◽

Neural Network Technology

Nowadays, earthquake prediction is still a worldwide scientific problem, especially the prediction for short-term and imminent earthquake has no substantial breakthroughs. BP neural network technology has a strong non-linear mapping function which could better reflect the strong non-linear relationship between earthquake precursors and the time and the magnitude of a potential earthquake. In this paper, we selected the region of Beijing as the research area and 3 months as the prediction period. Based on BP neural network and integrated with the conventional linear regression method, a regional short-term integrated model was established, which gives the quantitative prediction for the earthquake magnitude. The results show that the earthquake magnitude prediction RMSE (root mean square error) of the integrated model reaches ± 0.28 Ms. Compared with conventional methods, the integrated model improves significantly. The new model has a good prospect to use BP neural network technology for earthquake prediction.

Download Full-text