Seismic Registration With a Deep Neural Network Constraint

Seismic registration is a strongly nonlinear and ill-posed optimization problem in the presence of large misalignments or intense noise. Traditional cross correlation-based methods or dynamic image warping (DIW) methods may fail in such situations. From the perspective of optimization, the constraint on the shift may provide a more stable solution. We propose using a deep neural network (DNN) to constrain and solve for the shift. The continuity of the shift is well preserved across different traces with the DNN constraint. In addition, deep learning (DL) optimization is specifically efficient for solving nonlinear problems. The input of the DNN is optimized to produce a shifting image that matches two seismic images. This method is self-supervised; that is, only seismic images and warped versions are required. In our method, the signal-to-noise ratio (SNR) of the predicted shifts is improved by at least 7 dB compared with the traditional DIW method.