这篇论文教你如何在不微调的情况下用SAM做地震图分割，用混合提示和属性选择就能提升效果，挺实用的。
本文提出一个零样本框架，将Segment Anything Model (SAM)用于地震解释，无需微调。框架包括两个组件：1) 根据地质目标对齐地震属性与可视化（如色图）；2) 混合提示策略，结合稀疏用户定义点提示与SAM内部特征激活生成的稠密掩码提示。在多个地质目标、数据集和提示配置下评估，发现地质目标感知的属性选择与混合提示可提升边界描绘和分割精度。结果表明，零样本SAM能达到有竞争力的分割性能，减少对标注数据的依赖。
Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts
The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation. The Segment Anything Model (SAM), in particular, offers powerful zero shot segmentation capabilities through prompt based interaction, thus making it a promising tool for seismic interpretation. However, most existing applications of SAM rely on fine tuning for specific geological targets, which requires extensive labeled data, incurs high computational cost, and often compromises the model's generalization capability. In this study, we introduce a principled framework for zero shot adaptation of foundation models to seismic data. The framework is built on two key components: (1) aligning seismic attributes and visualization choices (e.g., colormaps) with the geological target of interest, and (2) employing a hybrid prompting strategy that combines sparse user defined point prompts with dense mask prompts derived from SAM's internal feature activations. We systematically evaluate this framework across multiple geological targets, datasets, prompt configurations, and seismic attribute representations. Our results demonstrate that geologic target aware selection of seismic attributes and colormaps, combined with hybrid prompting, enhances the separability of geological features and improves boundary delineation and segmentation accuracy relative to point based prompting alone. Our findings show that, when these components are jointly applied, SAM can achieve competitive segmentation performance in a fully zero shot setting, thereby eliminating the need to retrain SAM for each geologic feature. This work establishes a practical and scalable pathway to leverage foundation models in seismic interpretation, reducing reliance on labeled data while preserving model generality.