Abstract
The conventional time migration method does not consider the attenuation caused by the viscoelasticity of the underground media during the imaging process. Therefore, the final imaging amplitude and phase include inaccuracies caused by attenuation. In this study, we develop a migration scheme to compensate for absorption and dispersion using an effective quality factor ( Q) estimation based on a back-propagation (BP) neural network. We use BP neural network technology to automatically estimate the effective Q value from stacked imaging data, thereby avoiding manual Q estimation using conventional methods. Our scheme can be incorporated into conventional seismic data-processing workflows. Furthermore, synthetic and field data sets are used to validate our scheme, which is used to acquire high-resolution images with low noise levels. In addition to developing a completely data-driven Q-value estimation strategy, this study demonstrates close integration of artificial intelligence, data mining, and conventional geophysics; our approach is appropriate for estimating the effective Q and has strong industrial application value and significance.
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