Xin Ju

A prestack depth migration (PSDM) scheme was developed that compensates for absorption and dispersion in the absence of a conventional Q model. The proposed scheme followed the Kirchhoff approach. Instead of applying a commonly used Q model, an offset-related effective Q model was proposed, which only and fully determines, at each spatial location, the local migrated results independent of the effective Qs in other locations. The offset-related effective Q was approximately described at each spatial point using a quadratic polynomial. The related three parameters were determined during migration using a group of migrated gathers obtained by multiple homogeneous Q migrations. This was completed by solving a three-parameter optimization problem at each spatial point, which maximizes the bandwidth of the stacked spectra of partially and fully stacked local migrated sections inside the window around the point. The proposed approach overcame the challenge to estimate an interval Q model in depth, as in conventional anelastic PSDM. The resulting effective Q deabsorption PSDM was applied to synthetic 2D data and 3D field data. Higher-resolution imaging results were obtained.

Building a macro Q model for deabsorption migration using surface reflection data is challenging owing to interferences of the reflections resulting from stacked thin layers. The effective Q approach gives an alternative way to overcome this difficulty. However, manual processing is involved for effective Q estimation. This restricts the use of denser grids in building an inhomogeneous Q model. We therefore incorporate deep learning into the effective Q approach, thus yielding a deep learning‐based Q model building scheme. The resulting scheme improves the manual effective Q estimation by simultaneously accounting for the imaging resolution and induced noises using two networks. Moreover, most manual processing is reduced in spite of denser grids in building a 3D Q model. One of the networks used is a 1D convolutional neural network that determines the optimal upper cut‐off frequency for a selected Q with an input of multi‐channel amplitude spectra, and another is a residual neural network that determines the optimal Q for a series of Q values with an input of multi‐channel imaging sections inside the selected small window filtered under the corresponding upper cut‐off frequencies. As a result, a Q model that improves the imaging resolution in the absence of amplification of noises is gained. Transfer learning is used, thus reducing the training cost when applied to different geological targets. We test our scheme using 3D field data. Higher resolution images without induced noises are obtained by a deabsorption migration using the Q model built and compared to those obtained by the migration without absorption compensation.