We have developed a method for finding microseismic hypocenters from data recorded by arrays of triaxial motion sensors. The method reconstructs the elastic time-series signatures for possible microseismic sources at any point in 3D space, using full-waveform migration of the recorded vector wavefield. The imaging condition for the migration is based on a semblance-weighted deconvolution between two or more reconstructed source signatures, requiring similarity and simultaneity of the reconstructed signatures. This imaging condition eliminates the need for an absolute timing of the data, gives optimum resolution for the location of the microseismic sources—better than correlation-based approaches—and ensures numerical stability by adapting to the signal and noise conditions of the data. Because the method eliminates time-consuming phase picking and traditional event association, it should be well suited for fully or semiautomated data processing. The method was tested by an application to field data acquired with arrays of three-component receivers in two deep wells. Nevertheless, the formulation is equally applicable to data acquired by a distribution of single three-component receivers, or local arrays of these, deployed at the surface or in one or several shallow wells.

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We present a new approach to generating 3D location maps for microseismic events from 3-component data. The method combines full-waveform vector migration with an imaging condition based on semblance-weighted deconvolution. The semblance-weighted deconvolution keys in on the signal-to-noise conditions of the data to give a high-resolution, low-noise estimate of the locations for the microseismic sources. As the method requires no explicit time picking or event association, it is well suited for an either fully-automated or a semi-automated process. Almost as a by-product, the method provides a natural measure of the uncertainty associated with the locations of the individual micro-earthquakes.

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