Full waveform inversion with image-guided gradient

The objective of seismic full waveform inversion (FWI) is to estimate a model of the subsurface that minimizes the difference between recorded seismic data and synthetic data simulated for that model. Although FWI can yield accurate and high-resolution models, multiple problems have prevented widespread application of this technique in practice. First, FWI is computationally intensive, in part because it typically requires many iterations of costly gradient-descent calculations to converge to a solution model. Second, FWI often converges to spurious local minima in the data misfit function of the difference between recorded and synthetic data. Third, FWI is an underdetermined inverse problem with many solutions, most of which may make no geological sense. These problems are related to a typically large number of model parameters and to the absence of low frequencies in recorded data. FWI with an image-guided gradient mitigates these problems by reducing the number of parameters in the subsurface model. We represent the subsurface model with a sparse set of values, and from these values, we use image-guided interpolation (IGI) to compute finelyand uniformly-sampled gradients of the data misfit function in FWI. Because the interpolation is guided by seismic images, gradients computed in this way conform to geologic structures and subsequently yield models that also agree with subsurface structures. Because models are parameterized sparsely, IGI makes the models more blocky than finelysampled models, and this blockiness from the model space mitigates the absence of low frequencies in recorded data. A smaller number of parameters to invert also reduces the number of iterations required to converge to a solution model. Tests with a synthetic model and data demonstrate these improvements.