Absolute acoustic impedance inversion using convolutional neural networks with transfer learning

Abstract

Acoustic impedance (AI) is a key parameter frequently used for characterizing reservoirs in the oil and gas industry. The absolute AI can be divided into background and high-wavenumber components, which are related to the background velocity and migrated reflectivity, respectively. The relative AI derived from the migrated images is commonly used, but it is generally limited by the wavenumber range of the migrated image. Owing to the advantage of deep learning methods in determining the relationships among data sets, a learning-based AI inversion method that fuses the background model and the migrated reflectivity is proposed to determine the absolute AI. We first acquire learning labels using a synthetic model, and the initial parameters of a convolutional neural network are obtained by training the mapping from the known background AI and the reflectivity to the labels. We then establish a transfer learning scheme to refine the mapping network parameters using the target data. Finally, the target field data are processed using the refined network. The Marmousi model is used as the label for pretraining, and the synthetic Sigsbee2A model and field data are used for validation. Numerical tests demonstrate the effectiveness of the proposed method for absolute AI fusion, and the transfer learning scheme performs well to constrain the inverted AI that has decent consistency with the background model and reflectivity of the target data.