Qingfeng Xue

An efficient GPU implementation for locating micro-seismic sources using 3D elastic wave time-reversal imaging

Locating micro-seismic events is of utmost importance in seismic exploration, especially when searching for unconventional oil and gas resources. The arrival-time-difference approach is the dominant source location method currently used in the field of micro-seismic source location. However, micro-seismic events can be generated by any arbitrary rock movement and are often accompanied by interference noise. Recordings show characteristics of complicated wavelets and low signal-to-noise ratios. Under such conditions, conventional triangulation methods may have difficulty producing reliable locations; time-reversal imaging micro-seismic event location techniques are more promising. Locating micro-seismic events must be performed on-site for real-time monitoring of hydraulic fracturing. Introducing wave equation imaging techniques when locating micro-seismic events will increase the computation time, thus complicating real-time site monitoring. Therefore, the use of graphics processing unit (GPU) devices to accelerate time-reversal imaging micro-seismic event location technology becomes imperative. Three-dimensional synthetic data examples have demonstrated that the GPU-based time-reversal imaging micro-seismic event location method is typically 18 times faster than the central processing unit (CPU)-based implementation. The performance boost afforded by the GPU architecture allows us to locate micro-seismic events in 3D at a lower hardware cost and in less time than has been previously possible. 3D elastic micro-seismic source locating by time-reversal imaging.Multi-GPU devices and new GPU communication features are used fast calculation.Comparisons with CPU results show significant computational efficiency improvement.

An application of full-waveform inversion to land data using the pseudo-Hessian matrix

AbstractFull-waveform inversion (FWI) is used to estimate the near-surface velocity field by minimizing the difference between synthetic and observed data iteratively. We apply this method to a data set collected on land. A multiscale strategy is used to overcome the local minima problem and the cycle-skipping phenomenon. Another obstacle in this application is the slow convergence rate. The inverse Hessian can enhance the poorly blurred gradient in FWI, but obtaining the full Hessian matrix needs intensive computation cost; thus, we have developed an efficient method aimed at the pseudo-Hessian in the time domain. The gradient in our FWI workflow is preconditioned with the obtained pseudo-Hessian and a synthetic example verifies its effectiveness in reducing computational cost. We then apply the workflow on the land data set, and the inverted velocity model is better resolved compared with traveltime tomography. The image and angle gathers we get from the inversion result indicate more detailed informati...