3-D Seismic Multihorizon Extraction Based on a Domain Adaptive Deep Neural Network

The 3-D seismic multihorizon extraction is crucial for 3-D sequence stratigraphy analysis and reservoir modeling. Deep neural networks (DNNs) often cause dislocated horizons in regions with complex geological structures, such as faults and unconformities. This issue arises from two main factors. First, obtaining horizon labels from real seismic data is subjective and expensive, resulting in existing DNNs lacking field seismic data labels, which limits their ability to extract multiple horizons across complex geological structures. Second, directly extracting multiple horizons using only seismic data reduces precision in discontinuous areas with faults and unconformities. To address these issues, this article proposes a domain adaptation layer based on multikernel maximum mean discrepancy (MK-MMD) and designs a domain adaptive DNN (DA-DNN) for seismic multihorizon extraction. We map synthetic and field seismic data to the reproducing kernel Hilbert space (RKHS) and use MK-MMD to minimize feature differences between them. Unlike the traditional multiscale Gaussian kernel function used in MK-MMD, this article constructs a hybrid kernel function that integrates multiscale Gaussian and multiscale Laplacian kernels. The multiscale Gaussian kernel evaluates local-to-global feature differences in continuous areas, whereas the multiscale Laplacian kernel captures rapid feature variations in complex geological structures. Finally, a few seismic horizons and fault attributes guide the training process of DA-DNN, further improving the prediction accuracy of multihorizon extraction. Synthetic and field seismic examples show our model can extract seismic multiple horizons more accurately in field seismic data and performs better in discontinuous areas.