Regularized elastic passive equivalent source inversion with full-waveform inversion: Application to a field monitoring microseismic data set

Abstract

One of the key goals of microseismic processing is accurate estimation of the source location. Using full-waveform information in passive-source data sets can potentially delineate microseismic sources. The accuracy of the compressional-wave and shear-wave velocities has a strong influence on the estimation of source locations and hence the reliability of the fracture detection. We have adopted a methodology for passive source and velocity inversion, in which the conventional source term of the elastic wave equation is represented by an equivalent source. The equivalent source term is composed of source images and source functions because it is inspired by elastic reflection waveform inversion. Thus, we update the source locations, source functions, and velocities simultaneously by using a waveform inversion scheme. In the 2D isotropic case, the source terms are defined by two source image components and three source function components. They provide an alternative representation of the source mechanism, usually defined by the moment tensor. Waveform inversion of passive events has severe nonlinearity due to the unknown source locations in space and their functions in time. We have thus used a source-independent objective function, based on convolving reference traces with modeled and observed data, to avoid cycle skipping caused by the unknown sources. We first synthetically examined our method on a modified Marmousi model. Then, by applying a nested inversion for these variables, our method also produces good estimation of the source and background velocity for real microseismic monitoring data. We use a ball-drop event to test the accuracy because the inverted source location should match the ball-seat location. For the uncontrolled events, the estimated source distribution using waveform inversion agrees with the local stress potential information. Although our method has a higher computational cost than traveltime- or migration-based methods, the estimated event locations have significantly improved accuracy.