Time-Lapse Seismic Inversion Using a 3D Neural Operator

Time-lapse (4D) seismic inversion is a key tool for monitoring subsurface changes caused by fluid injection and production, but model-based workflows require repeated forward and adjoint evaluations, a wavelet estimate for every vintage, and survey-specific regularization tuning. We propose a 3D neural operator framework for post-stack time-lapse impedance inversion. A U-shaped neural operator is trained on synthetic impedance models and their convolutional post-stack responses and serves as a mesh-independent, differentiable surrogate of the post-stack forward operator. During inversion, the operator is frozen, and the impedance model is updated by gradient descent with automatic differentiation using one set of settings for all vintages of a survey. On the Kimberlina synthetic CO2 model, the inversion reduces the error of the initial model for every vintage (relative error from 3.1–3.2% to 2.8–2.9%). On the Sleipner field data, the recovered impedance changes are concentrated inside an independently mapped CO2 plume (median change 4.7–7.3 times larger than outside), the predicted 4D data difference correlates with the observed one at r=0.92–0.93 inside the plume, and the operator is applied without retraining to the full 1001×468×249 survey grid. We also quantify the training and fine-tuning costs and discuss the limits of a learned convolutional surrogate.