Fault detection is a crucial step in seismic interpretation, which can be regarded as a segmentation task in computer vision. Existing deep learning methods train models using synthetic data. However, due to differences between synthetic and field data in signal-to-noise ratio (SNR), seismic resolution, and fault orientation, models trained on synthetic data may yield unreliable results when applied to field data. In this article, we assume that the features required for fault detection are irrelevant to nonfault features such as SNR and propose a cross-disentangled representation learning method for 3-D fault detection, called FaultCDR. FaultCDR comprises a fault encoder, a nonfault encoder, a seismic reconstructor, and a segmenter. It employs a cross-disentangled representation mechanism to decouple fault features and nonfault features. The cross-disentangled representation mechanism is achieved through the seismic reconstruction task of remixed fault/nonfault features and a self-supervised feature consistency task. The proposed orthogonal loss is used to ensure that fault features and nonfault features are irrelated. The decoupled pure fault features are finally fed into the segmenter for fault detection. Through intro-database and cross-database testing, we demonstrated the stability and generalization of FaultCDR in fault detection across different datasets. Comparative experiments with existing state-of-the-art (SOTA) fault detection methods reveal that FaultCDR achieves superior performance in both detection accuracy and visual quality.

Current data-driven salt body interpretation methods are mainly based on 2-D seismic slices and complete labeling training. The 2-D salt body prediction results of this kind of method lose the spatial continuity of salt body distribution after being restored to 3-D seismic space. With the difficulty in acquiring salt body labels in the field, it becomes crucial to use sparse 2-D labeling to guide the learning of 3-D networks. We have proposed a 3-D salt body segmentation method based on multiview collaborative regularization, called 3-D multiview co-regularization (SALT-MVCR). Innovatively, we designed a dual-view collaborative training paradigm for voxel-level seismic data and proposed a regional loss function applicable to 2-D sparse-salt body labeling, which solved the difficult problem of asymmetrically supervised sample learning. In addition, a cross-view prediction consistency loss was designed to improve the segmentation model’s understanding of the salt body information by restricting the parameter search space of a single view and solving the artifacts of the prediction result splicing problem. Experimental results show that after supervised training with only 1.56% of salt body labels, a Dice index of 90.6% has been achieved. The visualization of the 3-D salt body distribution also demonstrates that 3-D SALT-MVCR is capable of interpreting the complete salt body from the 3-D seismic body end-to-end and outperforms previous state-of-the-art methods in terms of segmentation performance.

Interpolation and reconstruction of seismic data are critical steps in geophysical exploration, with results largely dependent on the performance of the interpolation techniques and the available feature information in the data. The task becomes particularly challenging when faced with complex data loss scenarios, such as high proportions of random discrete missing data and large amounts of random continuous missing data. To address this challenge, we propose a new method: generative nonlocal a priori augmented Wasserstein generative adversarial network (GNP-WGAN). The method uses a non local prior extraction (NLE) module improved by an edge detection algorithm to capture the structural information of seismic data, and a generative multidimensional attention restorer (GMAR) designed based on causal and axial attention to generate a smooth and accurate generative nonlocal prior (GNP). The Wasserstein GAN with gradient penalty is then augmented with GNP for finer and more accurate seismic data reconstruction. Finally, the MS-SSIM-$L_{1}$loss function is introduced to improve the quality of the generator reconstruction. Experiments on synthetic and field seismic datasets demonstrate the superior performance of GNP-WGAN in reconstructing seismic data with complex missing cases. In addition, subsequent experiments show that our GNP can be easily integrated as a plug-in into most of the currently popular reconstruction models to improve the accuracy and structural integrity of the reconstruction results and also exhibits enhanced robustness.

Salt bodies are significant reservoir structures, and there are still difficulties in interpreting them end-to-end from 3-D seismic data. Conventional semi-supervised learning struggles with obtaining high-quality pseudo labels early on, affecting subsequent model performance. Moreover, complex background noise hinders the accuracy of salt body predictions, while a strategy of gradually feeding training blocks leads to fragmented and confusing results. To address these challenges and restore realistic subsurface salt profiles, we have proposed an innovative, fully automated, and refined 3-D salt interpretation method called 3D Salt-HSM. In this method, we have designed a hybrid semi-supervised training paradigm based on stable pseudo labels and multilevel consistency constraints. This approach allows us to obtain high-quality pseudo labels for salt bodies and fully explore their features in unlabeled segmented blocks. We have also introduced a multitask learning strategy for fine interpretation of salt bodies, ranging from image level to pixel level. This strategy helps alleviate the adverse impact of interfering textures on salt body prediction. In addition, we have incorporated a contextual feature fusion module (CFFM) based on the multiscale context of salt bodies. This module enables the network to capture the global information of seismic images and achieve fine-grained salt body interpretation. In our experiments on the SEAM and F3 seismic datasets, we utilized only 3% of the labels for supervised learning, while the remaining data were used for unsupervised learning and validation. The experimental results demonstrate that 3D Salt-HSM outperforms previous state-of-the-art (SOTA) methods in terms of salt body segmentation performance, producing highly satisfactory results.