Synthetic seismic data generation with deep learning

Abstract

Abstract We study the applicability of deep learning (DL) methods to generate acoustic synthetic data from 1D models of the subsurface. We designed and implemented a Neural Network (NN) and we trained it to generate synthetic seismograms (common shot gathers) from 1-D velocity models on two different datasets: one obtained from published results and the other generated by Finite Differences (FD) numerical simulation. We furthermore compared the results from the proposed model with the published one. Moreover, we tried to to add more flexibility to this methodology by allowing change of wavelet and the acquisition geometry. We obtained good results in terms of both computation efficiency and quality of prediction. The main potentialities of the work are the low computational cost, a high prediction speed and the possibility to solve complex non-linear problems without knowing the physical law behind the phenomenon, which could led great advantages in the solution also of the inverse problem. DL to generate 1-D acoustic synthetic seismograms without solving wave equation Solution to the 1-D problem through custom Recurrent Neural Network Retraining strategy to improve flexibility and applicability Computational complexity analysis.