Full Waveform Inversion: A Modular Approach for Flexibility and Speed

Full waveform inversion (FWI) is an increasingly popular algorithm for automatically improving earth models in seismic exploration. The method is incredibly computationally expensive because the earth model is built up with many iterations of forward modelling and reverse time migration (RTM). Current research is helping to reduce the number of iterations and improve the robustness of convergence, but the prohibitive cost of FWI makes running real world datasets impractical for many researchers. Furthermore, information regarding the geological region can be used to accelerate convergence. Therefore, an FWI implementation must be both high performance, and flexible. This commercial case study outlines our approach to minimizing the cost of a flexible implementation.

The key feature of our approach is that we divide the algorithm into low cost and high cost steps. Fortunately, the low cost steps in the FWI algorithm are also the ones subject to the most research. The extremely high cost full wave modelling steps are comparatively consistent between variations of the FWI algorithm. Therefore, the full wave modelling is hardware optimized and all other steps can be quickly rewritten in Python.