Multitask Full Attention U-Net for Prestack Seismic Inversion

Deep learning has been widely used in seismic inversion. Since the label data obtained in production is actually a small amount of one-dimensional (1D) well-log data, most deep learning based seismic inversion is 1D trace-to-trace method based on poststack seismic data. However, the 1D algorithm does not take seismic data spatial correlation into consideration and the results lack of stability with good lateral continuity. Meanwhile, it is still a challenge to obtain high-precision multi-parameter inversion results simultaneously for prestack inversion. To mitigate the above issues, we propose a 2D Multi-task Full Attention U-Net for prestack three-parameter inversion. The proposed network can capture geological structural features in shared information and allow each task to automatically learn task-specific attention weights in a separate branch. The full attention mechanism fuses the shared features and the internal features of each task in stages, and pays attention in both channels and feature maps. Moreover, a joint hybrid loss function based on mean squared error and structural similarity is used to further enhance the continuity and resolution of inversion results. To obtain sufficient training samples, we generate a large number of small sample patches by sliding window both laterally and vertically with equidistant around the wells. In order to obtain stable inversion results, we incorporate the initial models and feed them into the network together with seismic data as input. Experiments on synthetic and field data show that our proposed method can simultaneously obtain three-parameter inversion results with high vertical resolution and transverse continuity.