A computer implemented method for building a velocity model for a geographical area of interest, GAI, comprising: acquiring by receivers (120, 204) data of reflected seismic waves; determining a ray based tomography velocity image of said GAI using said acquired data; determining a high resolution velocity guide, HRVG, image of said GAI using said acquired data; scaling said determined HRVG image of said GAI in order to determine a velocity perturbation; adding the velocity perturbation to the ray based tomography velocity image to determine an updated ray based tomography velocity image; determining whether said updated ray based tomography velocity image has experienced convergence by determining whether a cost function of said ray based tomography velocity image does not improve compared to a previously determined cost function value of said ray based tomography velocity image; and if the cost function has not improved over the cost function, taking said updated ray based tomography velocity image as said ray based tomography velocity image and proceeding to the step of determining a high resolution velocity guide image of said GAI using said acquired data, said method being characterized in that the step of determining an HRVG image comprises: using one of a final or intermediate result of full waveform inversion method, and wherein the step of using a final or intermediate result of full waveform inversion, FWI, comprises: determining an initial model; iteratively correcting the initial model using a gradient method, wherein, at each iteration, a direction of a model perturbation is computed from a weighted and filtered version of a gradient of a cost function consisting of the zero time lag cross-correlation of a forward propagated wave from sources and reverse-time propagated residuals, i.e. the misfit between data and synthetics, from the receivers. acquiring by receivers (120, 204) data of reflected seismic waves; determining a ray based tomography velocity image of said GAI using said acquired data; determining a high resolution velocity guide, HRVG, image of said GAI using said acquired data; scaling said determined HRVG image of said GAI in order to determine a velocity perturbation; adding the velocity perturbation to the ray based tomography velocity image to determine an updated ray based tomography velocity image; determining whether said updated ray based tomography velocity image has experienced convergence by determining whether a cost function of said ray based tomography velocity image does not improve compared to a previously determined cost function value of said ray based tomography velocity image; and if the cost function has not improved over the cost function, taking said updated ray based tomography velocity image as said ray based tomography velocity image and proceeding to the step of determining a high resolution velocity guide image of said GAI using said acquired data, said method being characterized in that the step of determining an HRVG image comprises: using one of a final or intermediate result of full waveform inversion method, and wherein the step of using a final or intermediate result of full waveform inversion, FWI, comprises: determining an initial model; iteratively correcting the initial model using a gradient method, wherein, at each iteration, a direction of a model perturbation is computed from a weighted and filtered version of a gradient of a cost function consisting of the zero time lag cross-correlation of a forward propagated wave from sources and reverse-time propagated residuals, i.e. the misfit between data and synthetics, from the receivers. using one of a final or intermediate result of full waveform inversion method, and wherein the step of using a final or intermediate result of full waveform inversion, FWI, comprises: determining an initial model; iteratively correcting the initial model using a gradient method, wherein, at each iteration, a direction of a model perturbation is computed from a weighted and filtered version of a gradient of a cost function consisting of the zero time lag cross-correlation of a forward propagated wave from sources and reverse-time propagated residuals, i.e. the misfit between data and synthetics, from the receivers. determining an initial model; iteratively correcting the initial model using a gradient method, wherein, at each iteration, a direction of a model perturbation is computed from a weighted and filtered version of a gradient of a cost function consisting of the zero time lag cross-correlation of a forward propagated wave from sources and reverse-time propagated residuals, i.e. the misfit between data and synthetics, from the receivers.