Seismic Traveltime Tomography Using Transfer Learning

Researchers have sought to overcome the limitations of traditional seismic inversion methods, such as full-waveform inversion (FWI), by applying deep learning techniques. Two primary approaches have emerged: the supervised learning approach, which uses an extensive dataset of seismic information to train a network, and the network parameterization approach, which treats the network's weights as parameters for inversion. Within the supervised learning approach, the use of time-domain wavefields as input has led to graphics processing unit (GPU) memory limitations, confining inversions to small-scale synthetic velocity models. For the network parameterization approach, field-scale synthetic velocity models have demonstrated strong inversion capabilities, yet the optimal initial weight set of the network has remained unexplored. This article introduces an innovative deep learning traveltime tomography method that applies network parameterization via transfer learning. Our findings indicate that weights refined through supervised learning capture essential features of the velocity models, thereby enhancing subsequent inversion using network parameterization. The study leverages a transfer learning strategy to enhance the robustness of network parameterization inversions. For supervised learning, we generate field-scale synthetic velocity models and their corresponding first-arrival travel times for seismic waves as inputs, bypassing the full time-domain wavefields. Subsequently, the method applies transfer learning to network parameterization. This approach reduces the computational demand of supervised learning and establishes an effective starting point for network parameterization. The numerical examples reveal that this novel deep learning traveltime tomography method outperforms both network parameterization with random weight initialization and conventional traveltime tomography in producing superior inversion results.