WATCH THE PRESENTATIONIn theory, Full waveform inversion (FWI) is a non-linear and global optimization algorithm that seeks to find a high-fidelity, high-resolution quantitative model of the subsurface by using all information in the recorded seismic waveforms. In practice, FWI is implemented as part of a workflow that contains iterative modeling and migration steps together with constrained parameter management.
Productivity enhancements to FWI can be obtained via the implementation of deep neural nets to the initial and final stages of the workflow: dynamic recurrent neural nets for time series analysis and convolutional neural nets for the characterization of features in the highly dimensional pre/post stack image cubes. A few general-purpose Deep Learning frameworks exist and will be reviewed given the requirements for a industry-specific implementation.
