Q model building, which is conventionally done in the data space using ray-based tomography, is a notoriously challenging problem due to issues like spectral interference, low signal-to-noise ratio, diffractions, and complex subsurface structure. To produce a reliable Q model, we present a new approach with two major features. First, this method is performed in the image-space, which uses downward-continuation imaging with Q to stack out noise, focus and simplify events, and provide a direct link between the model perturbation and the image perturbation. We develop two methods to generate the image perturbation for the following scenarios: the model with sparse reflectors and the model with dense reflectors. Second, this method uses wave-equation Q tomography to handle the complex wave propagation. Two synthetic tests on two different 2-D models with a Q anomaly shows the capability of this method on the model with sparse events. Tests with a modified SEAM model also demonstrate the feasibility of this method for the model with dense events.


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