In response to the challenge inherent in classical high-dimensional models of random ground motions, a family of simulation methods for nonstationary seismic ground motions was developed previously through employing a...In response to the challenge inherent in classical high-dimensional models of random ground motions, a family of simulation methods for nonstationary seismic ground motions was developed previously through employing a wave-group propagation formulation with phase spectrum model built up on the frequency components’ starting-time of phase evolution. The present paper aims at extending the formulation to the simulation of non-stationary random seismic ground motions. The ground motion records associated with N–S component of Northridge Earthquake at the type-II site are investigated. The frequency components’ starting-time of phase evolution of is identified from the ground motion records, and is proved to admit the Gamma distribution through data fitting. Numerical results indicate that the simulated random ground motion features zeromean, non-stationary, and non-Gaussian behaviors, and the phase spectrum model with only a few starting-times of phase evolution could come up with a sound contribution to the simulation.展开更多
This work focuses on variations of the Earth tidal factor and phase lag derived from groundwater observations before and after major earthquakes.It is based on an analysis of the data from four observational wells at ...This work focuses on variations of the Earth tidal factor and phase lag derived from groundwater observations before and after major earthquakes.It is based on an analysis of the data from four observational wells at boundaries between distinct active blocks of China mainland.These wells are also situated on several active fault zones and have exhibited considerable responses to the Wenchuan Ms8.0 earthquake of 2008 in China.We collected hourly records of water levels of these wells from 2007to 2009 and processed these data for analysis.The tidal factors,phase lags,and phase-difference changes of tidal residuals of each well were calculated.We found that when the Wenchuan quake happened,the tidal factors of the 4 wells were changing rapidly,while their phase lags and phase differences of tidal residuals declined swiftly,which may reflect the stress and strain changes of the well-aquifer system during the seismic generation.展开更多
To reveal the low growth rate of Acidithiobacillus ferrooxidans, a stochastic growth model was proposed to analyze growth curves of these bacteria in a batch culture. An algorithm was applied to simulate the bacteria ...To reveal the low growth rate of Acidithiobacillus ferrooxidans, a stochastic growth model was proposed to analyze growth curves of these bacteria in a batch culture. An algorithm was applied to simulate the bacteria population during lag and exponential phase. The results show that the model moderately fits the experimental data. Further, the mean growth constant （K） of growth curves is obtained by fitting the logarithm of the simulating population data versus the generation numbers with the different initial population number （N0） and initial mean activity of population （A0）. When No is 300 and 700 respectively, the discrepancy of K value is only 0.91%, however, A0 is 0.34 and 0.38 respectively, the discrepancy of K value is 19.53%. It suggests that the effect of A0 on the lag phase exceeds No, though both parameters could shorten the lag phase by increasing their values.展开更多
文摘In response to the challenge inherent in classical high-dimensional models of random ground motions, a family of simulation methods for nonstationary seismic ground motions was developed previously through employing a wave-group propagation formulation with phase spectrum model built up on the frequency components’ starting-time of phase evolution. The present paper aims at extending the formulation to the simulation of non-stationary random seismic ground motions. The ground motion records associated with N–S component of Northridge Earthquake at the type-II site are investigated. The frequency components’ starting-time of phase evolution of is identified from the ground motion records, and is proved to admit the Gamma distribution through data fitting. Numerical results indicate that the simulated random ground motion features zeromean, non-stationary, and non-Gaussian behaviors, and the phase spectrum model with only a few starting-times of phase evolution could come up with a sound contribution to the simulation.
基金supported by National Natural Science Foundation of China(Grant No.40930637)Special Project for Earthquake Science(Grant No.200808079)Subject Foundation of Ministry of Education for Doctor Candidates in Universities(Grant No.20100022110001)
文摘This work focuses on variations of the Earth tidal factor and phase lag derived from groundwater observations before and after major earthquakes.It is based on an analysis of the data from four observational wells at boundaries between distinct active blocks of China mainland.These wells are also situated on several active fault zones and have exhibited considerable responses to the Wenchuan Ms8.0 earthquake of 2008 in China.We collected hourly records of water levels of these wells from 2007to 2009 and processed these data for analysis.The tidal factors,phase lags,and phase-difference changes of tidal residuals of each well were calculated.We found that when the Wenchuan quake happened,the tidal factors of the 4 wells were changing rapidly,while their phase lags and phase differences of tidal residuals declined swiftly,which may reflect the stress and strain changes of the well-aquifer system during the seismic generation.
基金Project(50321402) supported by the Science Fund for Creative Research Groups of China project(2004CB619204) sup-ported by the National Key Fundamental Research Development Programof China
文摘To reveal the low growth rate of Acidithiobacillus ferrooxidans, a stochastic growth model was proposed to analyze growth curves of these bacteria in a batch culture. An algorithm was applied to simulate the bacteria population during lag and exponential phase. The results show that the model moderately fits the experimental data. Further, the mean growth constant （K） of growth curves is obtained by fitting the logarithm of the simulating population data versus the generation numbers with the different initial population number （N0） and initial mean activity of population （A0）. When No is 300 and 700 respectively, the discrepancy of K value is only 0.91%, however, A0 is 0.34 and 0.38 respectively, the discrepancy of K value is 19.53%. It suggests that the effect of A0 on the lag phase exceeds No, though both parameters could shorten the lag phase by increasing their values.
