For microseisimic monitoring it is difficult to determine wave modes and their propagation velocity. In this paper, we propose a new method for automatically inverting in real time the source characteristics of micros...For microseisimic monitoring it is difficult to determine wave modes and their propagation velocity. In this paper, we propose a new method for automatically inverting in real time the source characteristics of microseismic events in mine engineering without wave mode identification and velocities. Based on the wave equation in a spherical coordinate system, we derive a tomographic imaging equation and formulate a scanning parameter selection criterion by which the microseisimic event maximum energy and corresponding parameters can be determined. By determining the maximum energy positions inside a given risk district, we can indentify microseismic events inside or outside the risk districts. The synthetic and field examples demonstrate that the proposed tomographic imaging method can automatically position microseismic events by only knowing the risk district dimensions and range of velocities without identifying the wavefield modes and accurate velocities. Therefore, the new method utilizes the full wavefields to automatically monitor microseismic events.展开更多
The new technique that combines wave superposition with the fast Fourier transformation was introduced to simulate the nodal three-dimension relevant wind velocity time series of spatial structures. The wind velocity ...The new technique that combines wave superposition with the fast Fourier transformation was introduced to simulate the nodal three-dimension relevant wind velocity time series of spatial structures. The wind velocity field where the spatial structure is located is assumed to be homogeneous. The wind’s power spectral density is divided into frequency spectral function and coherency function and the spectral functions are transformed as the superposition coefficients. The wavelet analysis has excellent localized characters in both time and frequency domains, which not only makes wind velocity time series analysis more accurate, but also can focus on any detail of the objective signal series. The discrete wavelet transformation was adopted to decompose and reconstruct the discrete wind velocity time series. The stability of wavelet analysis for the wind velocity time series was also proved.展开更多
基金support jointly by projects of the National Natural Science Fund Project (40674017 and 50774012)the National Key Basic Research and Development Plan 973 (2010CB226803)
文摘For microseisimic monitoring it is difficult to determine wave modes and their propagation velocity. In this paper, we propose a new method for automatically inverting in real time the source characteristics of microseismic events in mine engineering without wave mode identification and velocities. Based on the wave equation in a spherical coordinate system, we derive a tomographic imaging equation and formulate a scanning parameter selection criterion by which the microseisimic event maximum energy and corresponding parameters can be determined. By determining the maximum energy positions inside a given risk district, we can indentify microseismic events inside or outside the risk districts. The synthetic and field examples demonstrate that the proposed tomographic imaging method can automatically position microseismic events by only knowing the risk district dimensions and range of velocities without identifying the wavefield modes and accurate velocities. Therefore, the new method utilizes the full wavefields to automatically monitor microseismic events.
文摘The new technique that combines wave superposition with the fast Fourier transformation was introduced to simulate the nodal three-dimension relevant wind velocity time series of spatial structures. The wind velocity field where the spatial structure is located is assumed to be homogeneous. The wind’s power spectral density is divided into frequency spectral function and coherency function and the spectral functions are transformed as the superposition coefficients. The wavelet analysis has excellent localized characters in both time and frequency domains, which not only makes wind velocity time series analysis more accurate, but also can focus on any detail of the objective signal series. The discrete wavelet transformation was adopted to decompose and reconstruct the discrete wind velocity time series. The stability of wavelet analysis for the wind velocity time series was also proved.
