We propose a new method for inverting source function of microseismic event induced in mining. The observed data from microseismic monitoring during mining are represented by a wave equation in a spherical coordinate system and then the data are transformed from the time-space domain to the time-slowness domain based on tomographic principle, from whichwe can obtain the signals related to the source in the time-slowness domain. Through analyzing the relationship between the signal located at the maximum energy and the source function, we derive the tomographic equations to compute the source function from the signals and to calculate the effective radiated energy based on the source function. Moreover, we fit the real amplitude spectrum of the source function computed from the observed data into the co-2 model based on the least squares principle and determine the zero-frequency level spectrum and the corner frequency, finally, the source rupture radius of the event is calculated and The synthetic and field examples demonstrate that the proposed tomographic inversion methods are reliable and efficient
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.
