High-sensitivity microseismic monitoring: Automatic detection and localization of subsurface noise sources using matched-field processing and dense patch arrays

Abstract

Recent advancements in seismic data acquisition and computational power have enhanced the deployment of dense seismic monitoring networks. The growing volume of recorded data requires the development of automated techniques to monitor and image zones of seismicity. We have developed an automatic detection and localization method that demands minimal a priori information for retrieval of the spatial distribution of subsurface noise sources (including, but not limited to, microseismic activity), in a reservoir and in the near vicinity during a hydraulic fracturing treatment. This method is based on matched-field processing (MFP), which takes advantage of the phase coherence that is recorded at dense arrays of sensors to localize noise sources. MFP is applied with a distributed set of patch arrays in the context of geophysics exploration. The MFP approach is applied to ambient noise recordings, and it provides results that are consistent with the classic localization methods applied to high-amplitude microseismic signals (in particular, using the relative template-based method). Furthermore, MFP provides enhanced sensitivity of detection and spatially extended information about structural heterogeneities. MFP opens a route to continuous, automatic, statistics-based, and high-sensitivity reservoir monitoring and imaging for geophysics exploration. Potential applications can also be envisaged for seismic monitoring of volcanic and geyser activities, and for other types of hydrothermal activity.