Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample set and a self-constructed convolutional neural network to recognize low-grade faults. We used Wu’s method to generate 500 pairs of low-grade fault samples to provide the data for deep learning. By combining the attention mechanism with UNet, an SE-UNet with efficient allocation of limited attention resources was constructed, which can select the features that are more critical to the current task objective from ample feature information, thus improving the expression ability of the network. The network model is applied to real data, and the results show that the SE-UNet model has better generalization ability and can better recognize low-grade and more continuous faults. Compared with the original UNet model, the SE-UNet model is more accurate and has more advantages in recognizing low-grade faults.
The development of supervised deep learning technology in seismology and related fields has been restricted due to the lack of training sets. A large amount of unlabeled data is recorded in seismic exploration, and their application to network training is difficult, e.g., fault identification. To solve this problem, herein, we propose an end-to-end training data set generative adversarial network Fault2SeisGAN. This network can expand limited labeled datasets to improve the performance of other neural networks. In the proposed method, the Seis-Loss is used to constrain horizon and amplitude information, Fault-Loss is used to constrain fault location information, and the Wasserstein distance is added to stabilize the network training to generate seismic amplitude data with fault location labels. A new fault identification network model was trained with a combination of expansion and original data, and the model was tested using actual seismic data. The results show that the use of the expanded dataset generated in this study improves the performance of the deep neural network with respect to seismic data prediction. Our method solves the shortage of training data set problem caused by the application of deep learning technology in seismology to a certain extent, improves the performance of neural networks, and promotes the development of deep learning technology in seismology.
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