The objective function for elastic FWI should be designed to update the model parameter enhancing tomographic term (low wavenumber component) rather than migration term (high wavenumber component) of the gradient in order to obtain robust inverted model which is close to global minimum. Firstly, by reconstructing objective functions using weighting factors which are different at each source and each frequency for the Gauss-Newton method, we propose a specific method to make the wavenumber spectrum of model parameter change vector uniform. Based on this weighting method, we prove that we can differently weight gradients of each shot and each frequency based on our own intention to control the magnitude of wavenumber spectrum of the model parameter change vector by applying additional weighting factors.


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