Efficient Bayesian Physics Informed Neural Networks for Surface Tomography via Ensemble Kalman Inversion

Surface travel-time tomography is a widely-used method to characterize the structure of the underground. However, conventional seismic tomography techniques often require a high-fidelity numerical forward model, which leads to significant computational burden and time consumption. Additionally, the inverse problem in travel-time tomography is prone to ill-posedness and lacks uncertainty quantification for the inferred results.