The structure-tensor analysis for optimal microseismic data partial stack

Microseismic monitoring of hydrofrac is an actively developing technology utilizing various acquizition arrays. In this paper we consider processing of microseismic data recorded by specific surface network geometry-patch arrays (far separated local receiver groups). The project aim is to produce an optimal partial stacking of the data within patches for improving a signal to noise ratio for microseismic events detection and location. We propose to use a structure-tensor analysis for estimating directions of coherency in the data, which can be used for data stacking for each patch. Unlike to the standard slantstacking method, we do not scan all possible directions, but receive them as eigenvectors of the structure tensor. We used the synthetic data for testing our approach in presence of random and coherent noise, in the case of interfering events. The testing showed that the structure-tensor analysis provides robust coherent summation results. We also discuss the usefulness of the structure-tensor attributes for detecting (triggering) the arriving wave and separating body wave from surface waves based on the apparent velocity.