Time reversal for wave refocusing and scatterer detection using machine learning

Conventional seismic migration and inversion are inherently limited in their ability to detect and characterize subsurface elements smaller than the seismic wavelength, such as faults, pinchouts, karsts, fractures, fluid contact, etc. However, those elements, playing an important role in seismic exploration and production, act as scattering objects, which can be effectively detected and positioned using the time reversal (TR) principle. We use TR to spatially localize subsurface sources in passive seismic scenarios and scatterers in active seismic surveys, both having the physical properties of a point diffractor. The method uses numerical back propagation of the time-reversed registered wavefield followed by an analysis of its obtained focusing, based on a supervised learning approach. In this novel approach, no imaging condition is applied. In addition, it does not require knowledge of the source wavelet and it accounts for multiple scattering. The usefulness of the method is demonstrated using synthetic and field examples.