The ability to detect small microearthquakes and identify their P and S phase arrivals is a key issue in hydrofracture downhole monitoring because of the low signal-to-noise ratios.We apply an array-based waveform correlation approach (matched filter) to improve the detectability of small magnitude events with mechanisms and locations similar to a nearby master event. After detecting the weak events, we use a transformed spectrogram method to identify the phase arrivals. We have tested the technique on a downhole monitoring dataset of the microseismic events induced by hydraulic fracturing. We show that, for this case, one event with a signal-to-noise ratio around 6dB, which is barely detectable using an array-stacked short-time average/long-time average (STA/LTA) detector under a reasonable false alarm rate, is readily detected on the array-stacked correlation traces. The transformed spectrogram analysis of the detected events improves P and S phase picking.
We investigated the structure of the P‐wave velocity (Vp) in the upper mantle beneath the southwestern edge of the Philippine Sea plate using traveltimes and waveforms from a dense, short‐period seismic array of approximately 75 seismographs in Taiwan. Using the seismograms from 39 shallow‐focus earthquakes, a composite seismic profile was constructed for a distance range of up to 4000 km. We picked the traveltimes of the first arrivals to construct a smoothly varying model for Vp which also served as a basis for static corrections. After band‐pass filtering and deconvolution to remove effects of source‐time functions and attenuation, we modelled the waveforms and triplicated arrivals in order to constrain the nature of the upper‐mantle discontinuities. Finally, after slant stacking, the entire observed wavefield was downward continued to obtain a regional model TS.406. This model includes a small low‐velocity zone between depths of 105 and 190 km, and then a gradual increase in Vp down to the depth of 406 km. At this discontinuity, Vp increases by 5.5 per cent. The P‐wave velocity then varies linearly in the transition zone with a moderate gradient of 0.0027 km s−1 km−1 until the base of the transition zone at 662 km, where Vp increases by 5.4 per cent. When compared with other models for the Philippine Sea region and average global models, model TS.406 has lower velocities above a depth of 300 km and then faster Vp in the upper part of the transition zone. For example, above 300 km the TS.406 model is slower than IASP91 by 1.5 per cent, but in the upper transition zone (406 to 571 km), it is faster by 1.2 per cent. Thus, the moderate‐amplitude anomaly of fast Vp in the middle of the transition zone reported by tomographic studies seems to occur at shallower depths. Furthermore, unlike the pronounced Northern Philippine Sea anomaly, Vp near the bottom of the transition zone is close to the global average under the southwestern edge of the Philippine Sea.
Theorectically, the perforation shot origin time T 0 affects the accuracy of the inverted velocity structure, and therefore the accuracy of subsequent microseismic event locations. The origin time can be obtained from perforation timing measurements or estimated from the picked arrival times. In order to investigate the role of origin time in velocity calibration, we designed two inversion procedures. In procedure A, T 0 is calculated during the Occam's inversion while T 0 is set to its true value in procedure B. A grid search locator is applied on both inverted models to produce two locations. We constructed three synthetic P-wave velocity models and add normally distributed random noise to the synthetic arrival times of all models. The noisy synthetic data are piped through procedure A to obtain location A and through procedure B to produce location B. Graphical analysis show that location A is closer to the true shot location than location B although both are close to each other. If we remove the data noise and repeat the test, location B is closer to the true shot than location A. It was observed that the inverted location A is better in terms of the distance from the true location if using noisy data and location B is better if using noise-free data. This indicates that uncertainties due to data noise cause our inconsistent observation and implies that perforation timing measurements are not necessary and may actually result in a less accurate velocity model.
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