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Locating microseismicity from surface monitoring arrays — A case study

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• published: 13 Jun 2014
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In a previous numerical study (Huang et al., 2013), we developed a new grid search algorithm to automatically locate microseismic (MS) events from streaming data recorded by surface monitoring survey. The symmetric nature of the semblance with respect to the origin time was used to identify the MS location. In this paper, we form a comprehensive workflow by extending this algorithm to include arrival time refinement using cross-correlation and nonlinear optimization similar to Geiger's method to update the location and origin time in order to minimize the residual between the observed and the theoretical arrivals. We demonstrate the efficiency of our workflow on seismograms continuously recorded by surface monitoring stations. We show that the automated grid search identified a significant amount of potential signals indicating the level of local seismic activities. The nonlinear optimization is applied to events with clear arrivals, and the vertical uncertainty due to the surface acquisition geometry and the coarse grid effect in the grid search are reduced. This algorithm can be directly applied to both natural earthquakes and reservoir stimulation monitoring.