Micro-earthquake monitoring with sparsely sampled data

Micro-seismicity can be used to monitor the migration of fluids during reservoir production and hydro-fracturing operations in brittle formations or for studies of naturally occurring earthquakes in fault zones. Micro-earthquake locations can be inferred using wave-equation imaging under the exploding reflector model, assuming densely sampled data and known velocity. Seismicity is usually monitored with sparse networks of seismic sensors, for example located in boreholes. The sparsity of the sensor network itself degrades the accuracy of the estimated locations, even when the velocity model is accurately known. This constraint limits the resolution at which fluid pathways can be inferred. Wavefields reconstructed in known velocity using data recorded with sparse arrays can be described as having a random character due to the incomplete interference of wave components. Similarly, wavefields reconstructed in unknown velocity using data recorded with dense arrays can be described as having a random character due to the inconsistent interference of wave components. In both cases, the random fluctuations obstruct focusing that occurs at source locations. This situation can be improved using interferometry in the imaging process. Reverse-time imaging with an interferometric imaging condition attenuates random fluctuations, thus producing crisper images which support the process of robust automatic micro-earthquake location. The similarity of random wavefield fluctuations due to model fluctuations and sparse acquisition is illustrated in this paper with a realistic synthetic example.