Optimal transport in full-waveform inversion: Analysis and practice of the multidimensional Kantorovich-Rubinstein norm
In the last ten years, full-waveform inversion has emerged as a robust and efficient high-resolution velocity model-building tool for seismic imaging, with the unique ability to recover complex subsurface structures. Originally based on a data fitting process using a least-squares cost function, it suffered from high sensitivity to cycle-skipping and was therefore of poor efficiency in handling large time shifts between observed and modelled seismic events. To tackle this problem, a common practice is to start the inversion using the low temporal frequencies of the data and selecting diving wave events. Complementary to this, the use of other cost functions has been investigated. Among these, cost functions based on optimal transport appeared appealing to possibly handle large time shifts between seismic events. Several strategies inspired by optimal transport have been proposed, taking into account the specificities of seismic data. Among them, the approach based on the Kantorovich-Rubinstein norm offers the possibility of the direct use of seismic data and an efficient numerical implementation allowing for a multidimensional (data coordinate space) application. We present here an analysis of the Kantorovich-Rubinstein norm, discussing its theoretical and practical aspects. A key component of our analysis is the back-propagated adjoint-source. We highlight its piecewise linearity, analyze its frequency content and amplitude balancing. We also emphasize the benefit of having a multidimensional implementation. Furthermore, we give practical rules for setting the tuning parameters of the numerical implementation. Our set of synthetic and field data examples demonstrate the improvements brought by the use of the Kantorovich-Rubinstein norm over least-squares full-waveform inversion, and highlight the improvements brought by the multidimensional approach over the one-dimensional one.