LAUSR

Deep learning (DL) methods have been widely applied in seismic inversion. However, one of the major challenges for DL-based seismic inversion is the time-shifted well-logging labels, which is resulted by the inaccurate time–depth relationship estimation during seismic well tie. Also, time-indexed phenomena of time-shifted well-logging labels may be squeezed, stretched, time ahead, or time lag, which can be considered as a typical noisy label problem in the DL field. In order to tackle the problem, we propose a dynamic time warping (DTW) loss-based closed-loop convolutional neural network (CNN) for seismic impedance inversion. First, DTW loss and cycle-consistency loss together constrain the closed-loop CNN training to optimize the weights of neural network. Second, the well-logging label will be corrected by warping the original well-logging label with the aligned path matrix during the iteration learning procedure, and the iteration termination criterion is reached if the similarity between the corrected well-logging label of the last iteration and that of the current iteration is larger than a given threshold. Third, the DTW error is suggested as the reasonable evaluation index in the blind-well test due to the time shift phenomena inevitably existed in the blind well. The experimental results on both synthetic data and real data demonstrate that the proposed method can effectively improve the inversion accuracy and spatial continuity.