Summary

In this work we apply the double-difference method to improve the accuracy of relative locations of microseismic events. First, we improve upon the P- and S-wave arrival times in an iterative way using crosscorrelation methods. Then we identify multiplets, or groups of events that have similar waveforms and source mechanisms, by crosscorrelation all events. Next, we apply the double-difference algorithm. The latter method minimizes the residuals between observed and predicted arrival times for pairs of microseismic events at each station, done by iteratively adjusting their differences. This relative location method is more appropriate for a dense cluster of events, hence we weight each observation based on the crosscorrelation coefficients and separation distances between events. This methodology also shows the different practical uses of normalized crosscorrelation functions for microseismic data analyses. Results are shown after applying this method to a microseismic data set from the mining industry, where a better linear feature is revealed after relocation.

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