Accurate traveltime modeling and inversion play an important role across geophysics. Specifically, traveltime inversion is used to locate microseismic events and image the Earth’s interior. Considered to be a relatively mature field, most of the conventional algorithms, however, still suffer from the so-called first-order convergence error and face a significant challenge in dealing with irregular computational grids. On the other hand, employing physics-informed neural networks (PINNs) to solve the eikonal equation has shown promising results in addressing these issues. Previous PINNs-based eikonal inversion and modeling schemes, however, suffer from slow convergence. We develop a new formulation for the isotropic eikonal equation by imposing the boundary conditions as hard constraints (HC). We implement the theory of functional connections (TFC) into the eikonal-based tomography, which admits a single loss term for training the PINN model. We demonstrate that this formulation leads to a robust inversion framework. More importantly, its ability to handle uneven acquisition geometry and topography providing an alternative answer towards the call for an energy-efficient acquisition setup.
|Original language||English (US)|
|Title of host publication||84th EAGE Annual Conference & Exhibition|
|Publisher||European Association of Geoscientists & Engineers|
|State||Published - 2023|