Wavefront tomography is an efficient and stable tool for the generation of smooth velocity models based on first and second-order attributes, which describe slope and curvature of the measured wavefronts. While slopes are relatively stable to determine, curvatures can become unreliable in the case of strong lateral heterogeneity. Since wavefront tomography is mainly driven by the misfit of modeled and measured wavefront curvatures, its convergence may be compromised by curvatures of bad quality. A possible solution to overcome this problem are diffractions that have a unique property: all measurements belonging to the same diffraction are connected to the same subsurface region. In recent work, we introduced an event-tagging scheme that automatically assigns a unique tag to each diffraction in the data. We propose to use this information to constrain the inversion by enforcing all diffracted measurements with the same tag to focus in depth, thus overcoming the sole dependency of wavefront tomography on second-order attributes. Results for diffraction-only data with vertical and lateral heterogeneity confirm that it is possible to obtain depth velocity models for zero-offset data without using curvature information and that the suggested approach may help to increase the stability of wavefront tomography in complex settings.


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