Automated microseismic event location using Master-Event Waveform Stacking (original) (raw)

Accurate and automated locations of microseismic events are desirable for many seismological and industrial applications. The analysis of microseismicity is particularly challenging because of weak seismic signals with low signal-to-noise ratio. Traditional location approaches rely on automated picking, based on individual seismograms, and make no use of the coherency information between signals at different stations. This strong limitation has been overcome by full-waveform location methods, which exploit the coherency of waveforms at different stations and improve the location robustness even in presence of noise. However, the performance of these methods strongly depend on the accuracy of the adopted velocity model, which is often quite rough; inaccurate models result in large location errors. We present an improved waveform stacking location method based on source-specific station corrections. Our method inherits the advantages of full-waveform location methods while strongly mitigating the dependency on the accuracy of the velocity model. With this approach the influence of an inaccurate velocity model on the results is restricted to the estimation of travel times solely within the seismogenic volume, but not for the entire source-receiver path. We finally successfully applied our new method to a realistic synthetic dataset as well as real data. The increasing number of microseismic monitoring networks for both seismological and industrial applications has led to an exponential growth of available microseismicity data in the last decade. These data typically contain a large number of weak seismic events, whose waveforms are often highly noise contaminated. Standard location routines based on automated phase picking, identification, and association tend to fail when dealing with such noisy data 1 or when simultaneous events occur 2. Therefore, an improved, fully automated, and noise robust procedure must be established in order to obtain high precision location for weak events. The main systematic limitation of traditional event location techniques is that the automated event identification is most commonly performed individually on single seismograms, thereby making little or no use of the coherency information between waveforms recorded at different stations 3. The increasing interest on microseismic monitoring operations , particularly for oil and gas reservoirs applications, motivated the development of new location methods based on waveform stacking techniques commonly used in reflection seismics 2. These methods exploit the coherence of the waveforms recorded at different stations and do not require any automated phase picking, identification and association procedure. The Source Scanning Algorithm (SSA) developed by Kao and Shan 4,5 represented the first attempt to use the signal coherency information for automated detection and location of earthquakes. In later years, several modified versions of this pioneer algorithm have been proposed and used for different applications , including monitoring of natural 6–8 and induced seismicity 1,9,10 , volcano seismology 11,12 , and landslides monitoring 13. The main advantage of this class of methods is that location results are robust even in presence of noisy waveforms 1,9,10. However, like any other absolute location method, their location performance still strongly depends on the accuracy of the a priori seismic velocity model 14,15. The adoption of inaccurate models may lead to large errors and uncertainties of the location results, which affect the output of further geological and geophys-ical analysis (e.g. estimation of source mechanism, event magnitude, etc.). The negative effect of velocity model inaccuracies on location results can be mitigated using relative location methods, which are generally based on source-specific station correction terms 14 or on the differential travel times for seismic sources that are next to