Detection, location, and source mechanism determination with large noise variations in surface microseismic monitoring

Abstract

Microseismic monitoring aims at detecting as weak events as possible and providing reliable locations and source mechanisms for these events. Surface monitoring arrays suffer from significant variations of noise levels across receiver lines. When using a large monitoring array, we use a stacking technique to detect microseismic events through maximizing the signal-to-noise ratio (S/N) of the stack. But some receivers with a high noise level do not contribute to improving the S/N of the stack. We have derived a theoretical concept for the proper selection of receivers that best contribute to the stack for a constant strength of a signal across the array. This receiver selection criterion, based on the assumption of constant signal amplitude, provides a robust estimate of the noise threshold level, which could be used to discard or suppress contribution from the receivers that do not improve the S/N of the stack. We found that limiting the number of receivers for stacking improves the location accuracy and reduces the computational cost of data processing. Although the assumption of a constant signal never holds in real-life seismic applications, the noise level varies across the surface receivers in a significantly wider range than the signal amplitude. These noise variations can also increase the uncertainty of the source mechanism inversion and should be accounted for. Synthetic and field data examples show that weighted least-squares inversion with receiver weighting according to the noise level produces more accurate estimates for source mechanisms compared to the inversion that ignores information about noise.