Elastic near-surface model estimation from full waveforms by deep learning

Strong near-surface heterogeneity poses a major challenge for seismic imaging of deep targets in arid environments. Inspired by the initial success of deep learning applications to inverse problems, we investigate the possibility of building nearsurface models directly from raw elastic data including surface and body waves in arid conditions. Namely, we train a convolutional neural network to map the data into the model directly in a supervised way on a part SEAM Arid synthetic dataset and evaluate its performance on a different part of the same dataset. The main feature of our approach is that we estimate the model as a set of 1D vertical velocity profiles, utilizing relevant subsets of input data from neighboring locations. This effectively reduces the data and label spaces for a more practical neural network application.