Conventional FWI using the least-squares norm (L2) as a misfit function is known to suffer from cycle skipping. We proposed the quadratic Wasserstein metric (W2) as a new misfit function for FWI. It has been proved to have many ideal properties with regards to convexity and insensitivity to noise. Unlike the L2 norm, W2 measures not only amplitude differences, but also global phase shifts, which helps to avoid cycle skipping issues. We propose two ways of using the W2 metric in FWI: trace-by-trace comparison and global comparison. Numerical results on synthetic models and field data demonstrate the promising future of this new misfit function.
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