Building a full-waveform inversion starting model from wells with dynamic time warping and convolutional neural networks

Abstract

Seismic full-waveform inversion (FWI) needs a feasible starting model because otherwise it might converge to a local minimum and the inversion result might suffer from detrimental artifacts. We have built a feasible starting model from wells by applying dynamic time warping (DTW) localized rewarp and convolutional neural network (CNN) methods alternatively. We use the DTW localized rewarp method to extrapolate the velocities at well locations to the nonwell locations in the model space. Rewarping is conducted based on the local structural coherence, which is extracted from a migration image of an initial infeasible model. The extraction uses the DTW method. The purpose of velocity extrapolation is to provide sufficient training samples to train a CNN, which maps local spatial features on the migration image into the velocity quantities of each layer. Furthermore, we design an interactive workflow to reject inaccurate network predictions and to improve CNN prediction accuracy by incorporating the Monte Carlo dropout method. We have determined that our method is robust against kinematic incorrectness in the migration velocity model, and it is capable of producing a feasible FWI starting model.