Deep-learning-based Q model building for high-resolution imaging

Abstract

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.