Focal mechanism determination for microseismic events and its application to the left bank slope of the Baihetan hydropower station in China
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Determination of the focal mechanism of microseismic (MS) events occurring in rock engineering plays an important role in hazard evaluation and forecasting. A complete moment tensor inversion strategy is proposed to analyse MS events recorded at the left bank slope of the Baihetan hydropower station, southwest of China. A coordinate system rotation procedure is adopted to construct a reliable 1-D velocity model for the Green’s function calculation. The layered velocities are accurately determined by inversion using the recorded blasts and geological survey results. To reduce the influence of noise contamination, only the waveforms in the frequency range of 60–150 Hz are analysed. Before moment tensor inversion, a relocation procedure using an eikonal solver is performed to accurately locate seismic sources. A robust global optimization routine is applied to maximize the objective function, thereby determining the focal mechanism. The proposed method is tested using a hypothetical seismic source in the sensor network used for monitoring MS events in the rock slope. The synthetic test shows that the method is robust and can provide accurate solutions when subject to location error, noise contamination and velocity model disturbances. The analysis results of the 12 MS events show that the moment tensors characterizing MS events present significant non-double-couple components and that the majority of the MS events have a strike orientation in accordance with that of the main staggered zones.
KeywordsFocal mechanism Rock slope Microseismic events Waveform matching Non-double-couple components
The authors are grateful for the financial support from the National Natural Science Foundation of China (51679158), National Program on Key basic Research Project (No. 2015CB057903) and the Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology) (No. SKLGP2016K018).
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