Receiver orientation and event back-azimuth estimation for downhole microseismic monitoring using a probabilistic method based on P-wave polarization - Yu, Zhichao; Huang, Dian; Tan, Yuyang; He, Chuan (Frontiers in Earth Science, 2023)
Microseismic event back-azimuth is an indispensable parameter for source localization in downhole microseismic monitoring, and the accurate orientation of horizontal components of downhole seismic receivers is vital for reliably determining the event back-azimuth. Variation in the monitoring data quality may jeopardize the accuracy of receiver orientation which will further affect the event back-azimuth estimation. To mitigate this issue, we proposed a new probabilistic method based on P-wave polarization analysis for receiver orientation and event back-azimuth estimation. The algorithm constructs the von Mises distribution function using the polarization angle and corresponding rectilinearity of the P-wave, then determines the target angle using the maximum of the probability function. The receiver having the highest rectilinearity from the active-source event is used to quantify a reliable absolute orientation angle, and the relative orientation angles are calculated by the probability distributions based on the measurement angle differences and the associated averages of rectilinearity from all events. After receiver orientation, the P-wave polarization angles with different rectilinearity values are applied to construct the probability distribution functions to estimate the event back-azimuths. By using high-quality events and multi-receiver recordings, our methodology can greatly reduce the unintentional error in receiver orientation and increase event back-azimuth accuracy. We investigate the feasibility and reliability of the proposed method using both synthetic and field data. The synthetic data results demonstrate that, compared to the conventional methods, the proposed method can minimize the variance of the receiver orientation angle and back-azimuth estimation. The weighted standard deviation analysis demonstrates that the proposed method can reduce the orientation error and improve the event back-azimuth accuracy in the field dataset.