Automated microseismic event location by amplitude stacking and semblance

Abstract

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.

Application of Waveform Stacking Methods for Seismic Location at Multiple Scales

“…More recently, these methods have been naturally adapted to analyze low-frequency seismic events at larger scales [28,50], where a large depth uncertainty exists. There are also emerging applications for seismic events with comparably high-frequency content at smaller scales-e.g., rock burst and acoustic emission, where the issues of location uncertainty and velocity reliability are matters of considerable concern [32,51]. The events tested in this study have relatively high signal-to-noise ratio, and waveforms recorded with dozens of receivers can recover the source energy quite well.…”

“…In this application study, we estimate the performance of stacking-based methods by only evaluating the reliability of spatial source parameters. There are numerous CFs being studied and utilized, including waveform envelope, semblance, kurtosis, and short-term average to long-term average ratio (STA/LTA) [28,32,[34][35][36][37][38]. The STA/LTA is used in this study, and different lengths of short-and long-term windows are adopted for waveforms recorded at different scales (see Table 1 for more details).…”

“…Stacking-based methods have been applied to field data at multiple scales, including experimental microseismic/acoustic emission events, mining-induced seismicity, hydraulic-fracturing-induced seismicity, volcanic-tectonic seismicity, and regular earthquakes [27][28][29][30][31][32]. However, more systematic benchmarking studies of these approaches across scales are still needed.…”

Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

“…Besides the imaging operator, the PWS methods also differ in preprocessing of the input waveforms and the event detection and location detection criteria. There are various CFs adopted to improve the SNR and compensate the side effects of source radiation patterns; for example, the input data are converted to envelope (Gharti et al, ; Liao et al, ; Zeng et al, ); semblance (Furumoto et al, ; W. Zhang & Zhang, ; Staněk et al, ; C. Zhang et al, ); short‐term average to long‐term average ratio (STA/LTA; Drew et al, ; Grigoli et al, ; L. Li et al, ); kurtosis (Langet et al, ; Poiata et al, ); multichannel coherency (Shi, Angus, et al, ). …”

“…There are various CFs adopted to improve the SNR and compensate the side effects of source radiation patterns; for example, the input data are converted to 1. envelope (Gharti et al, 2010;Liao et al, 2012;Zeng et al, 2014); 2. semblance (Furumoto et al, 1990;W. Zhang & Zhang, 2013;Staněk et al, 2015;C. Zhang et al, 2019); 3. short-term average to long-term average ratio (STA/LTA; Drew et al, 2013;Grigoli et al, 2014;L.…”