Without the need to pick the arrival times of P- and S-waves, migration-based location methods, such as semblance-based and amplitude-stacking-based location methods, are best applied to microseismic events. By comparing and analyzing the advantages and disadvantages of these two methods, we have developed a new location method using amplitude information and semblance. First, we use the two-point ray-tracing method to calculate the traveltime of body waves from the trial point to each receiver, which determines the time-window positions of the P- and S-waves on all traces. Then, we calculate the semblance of the waveforms and the amplitude stacking of the ratio between the short-time average and the long-time average is computed upon the original waveform over the windows. Finally, the semblance weighted by amplitude stacking is used to image the spatial location of the microseismic events. Using experimental and synthetic data considering different factors that may affect the location result (e.g., the signal-to-noise ratio of the waveforms, the scale of the observation array, and the horizontal and vertical distances from the source to fracture zones), we perform microseismic event location with all three methods. According to the source imaging results from experimental and synthetic tests, the semblance method has great location uncertainty in the radial direction but it has good constraints in the circumferential direction; the amplitude-stacking method exhibits the opposite result; and the weighted-semblance method has good constraints in the circumferential and radial directions because it inherits the advantages of semblance-based and amplitude-stacking-based methods. Therefore, compared with existing migration-based location methods, our weighted-semblance method indicates stronger stability and lower location uncertainty, even when downhole monitoring is conducted with a limited aperture of the receiver array.
Data-driven versus self-similar parameterizations for Stochastic Advection by Lie Transport and Location Uncertainty
