Joint location and source mechanism inversion of microseismic events: benchmarking on seismicity induced by hydraulic fracturing

0434520 - ÚSMH 2015 RIV GB eng J - Journal Article
Anikiev, D. - Valenta, Jan - Staněk, František - Eisner, Leo
Joint location and source mechanism inversion of microseismic events: benchmarking on seismicity induced by hydraulic fracturing.
Geophysical Journal International. Roč. 198, č. 1 (2014), s. 249-258. ISSN 0956-540X. E-ISSN 1365-246X
R&D Projects: GA ČR GAP210/12/2451
Institutional support: RVO:67985891
Keywords : inverse theory * probability distributions * wave scattering and diffraction * fractures and faults
Subject RIV: DB - Geology ; Mineralogy
Impact factor: 2.560, year: 2014

Seismic monitoring can greatly benefit from imaging events with a low signal-to-noise ratio (SNR) as the number of the events with a low signal grows exponentially. One way to detect weaker events is improvement of a SNR by migration-type stacking of waveforms from multiple stations. We have developed a new method of location of seismic events that involves stacking of seismic phases and amplitudes along diffraction traveltime curves to suppress noise and detect seismic events with a SNR lower than that on individual receivers. The stacking includes polarity correction based on a simultaneous seismic moment tensor inversion and detection algorithm on the stack function. We applied this method to locate microseismicity induced by hydraulic fracturing. First we calibrated the velocity model by locating perforation shots at known locations. Then we processed 3 d of data from microseismic monitoring of shale stimulation and benchmarked migration-type locations of the largest events that were manually located. The detected and located events induced by hydraulic fracturing in this case study are mostly shear events forming narrow bands along the maximum horizontal stress direction approximately 100 m above the injection intervals. The proposed technique is fully automated and feasible for real-time seismic monitoring.
Permanent Link: http://hdl.handle.net/11104/0238548