Nonlinear LS-RTM based on seismic scale separation and full wavefield multi-parameter FWI

Least-squares migration (LSM) images the subsurface through an inversion process and seeks the reflectivity model that best explains the measured data. This data-fitting process will implicitly compensate for wavefield propagation effects and limited resolution seen with conventional imaging. These favourable properties have made it the method of choice in complex geological settings. In this paper, we will discuss the various properties and implementations of LSM and Full Waveform Inversion (FWI) and suggest a new nonlinear least-squares reverse time migration (LS-RTM) implemented in the framework of FWI. The new inversion estimates the earth reflectivity while updating the velocity model at every iteration. We demonstrate the new solution on a field data example from the Norwegian Sea.