Nonlinear Processing Methods for Detection and Location of Microseismic Events

Abstract

Locating microseismic events using a method such as coalescence microseismic mapping (CMM) is much more effective if the underlying detection function, such as STA/LTA, has reliably detected the events. This in turn depends on the SNR of the data that the detection function has been applied to. These issues are especially important for surface microseismic monitoring, where the SNR of the recorded data is significantly less than that of borehole data. In this paper, we introduce novel nonlinear stacking methods to improve automatic detection and location of weak seismic events that are typically below the background noise-level. We also introduce several novel methods that have applications for detection and location of signals with varying source radiation patterns that have a phase change. We show the outline of the methods on synthetic data and also demonstrate application to real data acquired during surface microseismic monitoring. In the real data case we compare one nonlinear stacking method with the conventional stack for a series of perforation shot arrivals and show how the signal-to-noise ratio is significantly improved for a real event, and how this improves the event detection and location sensitivity by a combination of signal-to-noise ratio improvement and noise discrimination.