Q Estimation and Attenuation Compensation for Seismic Data Using Semi-Supervised Multitask 2-D V-Net With Transfer Learning

Conventionally, we usually carry out Q estimation first and then perform seismic attenuation compensation (Ac). In this article, we propose a method based on semi-supervised multitask 2-D V-Net combined with transfer learning to simultaneously conduct Q estimation and Ac for seismic data. The semi-supervised network consists of an inversion network and a forward network, both of which are 2-D V-Net, and two channels are used to accomplish two tasks. The network pretrained on the synthetic data generated by the typical model is transferred to the target field data. The loss between the predictions of the inversion network and the label data, along with the loss between the seismic data obtained by the forward network and the input seismic data, is used to jointly guide the network training. As a result of fully exploiting the synthetic data, labeled data, and unlabeled seismic data for network training, the demand for labeled data is reduced. For the situation where the number of well logs is extremely small, a two-step strategy is developed. First, a certain number of labels are generated using a conventional method based on stratigraphic structure-constrained molecular decomposition to train the network, and then the labels produced by well logs are used to further train the network. Both synthetic and field examples demonstrate that the two-step strategy enables our method to simultaneously achieve Q and compensated seismic data with high accuracy using attenuated seismic data, even with only one well log. This approach, moreover, exhibits good robustness against noise.