Seismic fault image segmentation is crucial for interpreting subsurface geological structures, supporting geologists in resource exploration and structural analysis. However, current deep learning models struggle with single-architecture limitations and the distinctive characteristics of seismic faults, which are distinguished by elongated structures with uneven spatial distributions. To address these challenges, we propose TopSUMseg, a novel Topology-Aware Swin Transformer-Mamba framework for 3D seismic fault image segmentation. Our framework combines Swin Transformer’s local feature extraction with Mamba’s efficient sequence modeling, and boosts 3D spatial modeling in Mamba with a newly designed Global-Local Attention module (GLA). Additionally, we design a Topology-Aware Structural Constraint (TASC) to align predictions with ground-truth structures in the feature space, promoting the modeling of complex fault geometries. Experiments on Thebe, the largest public seismic dataset, demonstrate that TopSUMseg achieves state-of-the-art performance with OIS and ODS scores of 0.879 and 0.875, respectively. Trained entirely from scratch, TopSUMseg nonetheless achieves superior performance compared to extensively pre-trained counterparts. In addition, TopSUMseg maintains a significantly lower parameter count while achieving a favorable trade-off between segmentation performance and time complexity, making it a practical and generalizable solution for real-world seismic fault interpretation.

Multimodal large language models (MLLMs) have achieved significant progress in document understanding. However, complex layout reasoning, characterized by concise answers and cross-page integration, remains a challenge. Unlike conventional semantics-oriented tasks, this task demands accurate visual perception of fine-grained structural elements and logical reasoning across multi-page documents. Existing approaches primarily focus on information extraction and semantic understanding, limiting the capacity of fine-tuned autoregressive models to capture short-answer reasoning signals and generalize to complex layout structures. To address this, we propose the Layout-Aware Multi-Source Reasoning Decision Framework (LAMRD), which reframes complex layout reasoning as a decision-making task over multi-source reasoning paths. In the reasoning path construction stage, LAMRD generates layout-aware reasoning paths by integrating internal visual cues and external knowledge from three complementary perspectives: Visual Structural Awareness (VSA), Logical Reasoning Paths (LRP), and External Knowledge Augmentation (EKA). In the reasoning path decision stage, we employ Group Relative Policy Optimization (GRPO) to train a decision model that produces the final answer based on these paths. We conduct comprehensive evaluations using Qwen2.5-VL-7B-Instruct on the CEP-7K dataset, covering layout structure understanding, information extraction, and logical association. Experimental results demonstrate that LAMRD outperforms advanced MLLMs in accuracy, validating its effectiveness for complex document layout understanding.