Diffraction wave could be sensitive to the subsurface discontinuity, such as fractures or faults. This research aims to develop innovative technique to separate and image the diffraction waves from conventional reflection waves.

This research aims to use PP, PS, SS multicomponent seismic datasets to detect and characterize the subsurface features with strong anisotropic elasticity, such as fractures.

This research aims to use rock physics and machine learning techniques to estimate geomechanical or petrophysical properties of unconventional reservoir. We are working on the estimation of the TOC and Brittleness of the Tuscaloosa Marine Shale by using machine learning technique.

Time-lapse seismic surveys refer to repeated seismic surveys, which can help to capture the subsurface rock properties changes. However, it could be very difficult to keep the consistence of the repeated surveys with baseline survey, which could cause problematic utilization of the time-lapse seismic data. We plan to improve the reliability of the time-lapse seismic analysis, especially two kinds of seismic attributes: amplitude and travel-time.