Detecting small amplitude signal and transit times in high noise: Application to hydraulic fracture monitoring

A workflow is presented for the automatic detection and accurate time picking of weak events, such as microseismicity induced by hydraulic fracturing, in data collected by an array of multi-component acoustic sensors. These events are embedded in strong noise that could be spatially and temporally correlated. The proposed workflow comprises two steps. In the first, a new statistical test is applied to detect the time windows that contain coherent arrivals across components and sensors in the multicomponent array and to indicate the confidence in this detection. The second is a time-picking step to accurately estimate the onset time of the arrivals detected above and measure the time delay across the array using a novel hybrid beamforming method incorporating the use of higher order statistics. Such an approach is designed to significantly improve the number of events that are detected as well as the level of noise that can be handled. In the context of hydraulic fracturing, this could enhanced the coverage and mapping of the fractures. Examples on real field data are presented indicating the effectiveness of this workflow.