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.

翻译：大规模预训练基础模型在计算机视觉领域的出现显著提升了视觉数据解释的效率。其中，Segment Anything模型（SAM）通过基于提示的交互展现出强大的零样本分割能力，成为地震解释领域极具前景的工具。然而，现有SAM应用多针对特定地质目标进行微调，这需要大量标注数据、产生高昂计算成本，且往往损害模型的泛化能力。本研究提出了一种将基础模型零样本适配至地震数据的规范化框架。该框架基于两个关键组件：（1）将地震属性及可视化选择（如色图）与目标地质特征对齐；（2）采用混合提示策略，结合稀疏的用户定义点提示与源自SAM内部特征激活的密集掩码提示。我们针对多种地质目标、数据集、提示配置及地震属性表征对该框架进行了系统评估。结果表明，相较于仅使用点提示，地质目标感知的地震属性与色图选择，结合混合提示策略，能增强地质特征的可分离性，并改善边界刻画与分割精度。研究显示，当这些组件协同应用时，SAM可在完全零样本设置下实现具有竞争力的分割性能，从而无需针对每个地质特征重新训练SAM。本工作为在地震解释中利用基础模型建立了实用且可扩展的路径，在保持模型通用性的同时减少了对标注数据的依赖。