The migration-based microseismic event location methods using waveform stacking algorithms are widely used for hydrofracturing monitoring. These methods have the advantage of not requiring the accurate frst arrival time around a detected event, which is more suitable for noisy data than classical travel time-based methods. However, accuracy of these methods can be afected under the condition of relatively low signal-to-noise ratio (SNR). Therefore, in order to enhance the location accuracy of microseismic events in a borehole system, we have proposed a migration-based location method using improved waveform stacking with polarity correction based on a master-event technique, which optimizes the combination way of P- and S-wave waveform stacking. This method can enhance the convergence of the objective function and the location accuracy for microseismic events as compared to the conventional waveform stacking. The proposed method has been successfully tested by using synthetic data example and feld data recorded from one downhole monitoring well. Our study clearly indicates that the presented method is more viable and stable under low SNR.
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Most of the microseismic signals have low signal-to-noise ratio (SNR) due to the strong background noise, which makes it difficult to locate the first arrival time. Both accuracy and stability of conventional methods are poor in this situation. To overcome this problem, here we proposed a new method based on the adaptive Morlet wavelet and principal component analysis process in wavelet coefficients matrix. The three components of microseismic signal make it possible to extract the features in wavelet coefficients domain. Then the reconstructed signal from weighted features presents an obvious first arrival. Tests on synthetic signals and real data provide a solid evidence for its feasibility in low SNR microseismic signal.
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