Reflection and diffraction separation in the dip-angle common-image gathers using convolutional neural network

Abstract

In exploration seismology, reflections have been extensively used for imaging and inversion to detect hydrocarbon and mine resources, which are generated from subsurface continuous impedance interfaces. When the interface is not continuous and its size reduces to less than half-wavelength, reflected wave becomes diffraction. Reflections and diffractions can be used to image subsurface targets, and the latter is helpful to resolve small-scale discontinuities, such as fault plane, pinch out, Karst caves, and salt edge. However, the amplitudes of diffractions are usually much weaker than that of reflections. This makes it difficult to directly identify and extract diffractions from unmigrated common-shot or common-middle-point gathers. Migrating seismic data into a subsurface location for different reflector dip angles yields a dip-angle-domain common-image gather (DACIG). One DACIG represents the migrated traces at a fixed lateral position for different reflector dips. The reflection and diffraction have different geometric characteristics in DACIG, which provides one opportunity to separate diffractions and reflections. In this study, we present an efficient and accurate diffraction separation and imaging method using a convolutional neural network (CNN). The training data set of DACIGs is generated using one pass of seismic modeling and migration for velocity models with and without artificial scatterers, respectively. Then, a simplified end-to-end CNN is trained to identify and extract reflections from the migrated DACIGs that contain reflections and diffractions. Next, two adaptive subtraction strategies are presented to compute the diffraction DACIGs and stacked images, respectively. Numerical experiments for synthetic and field data demonstrate that the proposed method can produce accurate reflection and diffraction separation results in DACIGs, and the stacked image has a good resolution for subsurface small-scale discontinuities.