Joint microseismic moment-tensor inversion and location using P- and S-wave diffraction stacking

Abstract

The microseismic location methods based on diffraction stacking which does not require arrival picking can yield accurate and reliable source location for data with a low signal-to-noise ratio. However, due to the complex radiation pattern from a rupturing source, variation in the waveform polarities brings challenges to the diffraction-stacking based methods. The current implementations of joint source mechanism inversion and location methods which only use P-wave amplitudes have limitations in noise resistance and location accuracy. To mitigate those issues, we develop a new method for joint microseismic moment tensor inversion and event location using diffraction stacking with P- and S-waves amplitudes, both of which are used to invert for the moment tensor of a microseismic event, and then the inverted moment tensor is used to correct the waveform polarity changes before stacking. In addition, to expedite the large amount of calculations required for moment tensor inversion at each potential source position and origin time, we develop an optimized grid search scheme and implement the algorithm with GPUs. The proposed location method does not require manual picking of the first arrivals, and can automatically detect and locate microseismic events from continuous data. We first validated the method with two synthetic examples, and then applied it to a surface monitoring dataset for hydraulic fracturing at a shale gas well pad in the southern Sichuan Basin, China, where billions of cubic meters of shale gas are being produced annually. The locations of the microseismic events are nicely correlated with the fracturing stages and the determined source mechanisms are also consistent with the expected fracture growth. The proposed method is feasible for microseismic surface monitoring with dense nodal arrays and can provide important information for fracture growth and regional stress characterization.

A Novel Polarity Correction Method Developed on Cross Correlation Analysis for Downhole Migration-Based Location of Microseismic Events

“…In order to overcome the destructive interference, Trojanowski and Eisner considered that waveform stacking with polarization correction may yield more accurate locations compared to above absolute value-based methods [5]. Several authors measured the polarities through focal mechanism inversion [30][31][32][33][34][35]. Tian et al determined P-wave first-motion polarity by using deep learning [36].…”