来自NAMSS的通用多样化2D地震图像数据集
A General-Purpose Diversified 2D Seismic Image Dataset from NAMSS
AI总结：
Unicamp-NAMSS数据集通过多样化和广泛分布的2D地震剖面，为地球物理学中的机器学习研究提供支持，适用于预训练、自监督学习和领域适应等任务。
摘要：
我们介绍了Unicamp-NAMSS数据集，这是一个大规模、多样且地理分布广的迁移2D地震剖面集合，旨在支持地球物理学中现代机器学习研究。我们从国家海洋地震调查档案（NAMSS）构建了该数据集，该档案包含数十年来在多个地区、采集条件和地质环境中公开获取的海洋地震数据。经过全面收集和过滤过程后，我们获得了来自122个调查区域的2588个清洁且标准化的地震剖面，涵盖了广泛的垂直和水平采样特征。为确保可靠的实验，我们平衡了数据集，使没有任何调查主导分布，并将其划分为非重叠的宏观区域用于训练、验证和测试。这种区域不重叠的划分允许对未见的地质和采集条件进行稳健的泛化评估。我们通过定量和嵌入空间分析验证了该数据集，使用了卷积和基于变压器的模型。这些分析表明，Unicamp-NAMSS在区域内和跨区域之间表现出显著的变异性，同时在采集宏观区域和调查类型之间保持结构一致。与广泛使用的解释数据集（Parihaka和F3 Block）的比较进一步表明，Unicamp-NAMSS覆盖了更广的地震外观空间，使其成为机器学习模型预训练的有力候选。因此，该数据集为机器学习任务提供了宝贵的资源，包括自监督表示学习、迁移学习、基准测试监督任务如超分辨率或属性预测，以及研究地震解释中的领域适应。
英文摘要：
We introduce the Unicamp-NAMSS dataset, a large, diverse, and geographically distributed collection of migrated 2D seismic sections designed to support modern machine learning research in geophysics. We constructed the dataset from the National Archive of Marine Seismic Surveys (NAMSS), which contains decades of publicly available marine seismic data acquired across multiple regions, acquisition conditions, and geological settings. After a comprehensive collection and filtering process, we obtained 2588 cleaned and standardized seismic sections from 122 survey areas, covering a wide range of vertical and horizontal sampling characteristics. To ensure reliable experimentation, we balanced the dataset so that no survey dominates the distribution, and partitioned it into non-overlapping macro-regions for training, validation, and testing. This region-disjoint split allows robust evaluation of generalization to unseen geological and acquisition conditions. We validated the dataset through quantitative and embedding-space analyses using both convolutional and transformer-based models. These analyses showed that Unicamp-NAMSS exhibits substantial variability within and across regions, while maintaining coherent structure across acquisition macro-region and survey types. Comparisons with widely used interpretation datasets (Parihaka and F3 Block) further demonstrated that Unicamp-NAMSS covers a broader portion of the seismic appearance space, making it a strong candidate for machine learning model pretraining. The dataset, therefore, provides a valuable resource for machine learning tasks, including self-supervised representation learning, transfer learning, benchmarking supervised tasks such as super-resolution or attribute prediction, and studying domain adaptation in seismic interpretation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
