Seismic Traveltime Tomography Using Deep Learning

Seismic inversion techniques based on supervised deep learning have shown promising results with synthetic data targeting small areas. These techniques use seismograms as input data and subsurface velocity models as output data. However, their application to field-scale data for high-resolution final models demands huge computational resources and is currently impractical. To overcome this limitation, we propose a new approach called deep-learning traveltime tomography that predicts large-scale velocity models using only first-arrival traveltimes of seismic waves as input data. This approach reduces data size and speeds up network training. To train the network, we generate field-scale synthetic velocity models and corresponding first-arrival traveltimes, and then use them for supervised learning. We adopt a marine towed-streamer acquisition geometry to simulate field acquisition situations. Since a lightly trained network has limited generalization ability and the output has low resolution, we use the predicted results as initial models for subsequent conventional first-arrival traveltime tomography. This strategy mitigates the need for huge computational resources and the problem of dependency on the correct initial model of conventional traveltime tomography. Numerical examples demonstrate that the outputs of the lightly trained deep-learning network can enhance inversion results of conventional traveltime tomography for both synthetic and field data.