Abstract
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.
Abstract (translated)
我们介绍了Unicamp-NAMSS数据集，这是一个大型、多样且地理分布广泛的数据集合，包含迁移的二维地震剖面，旨在支持地球物理学领域的现代机器学习研究。该数据集由国家海洋地震调查档案（NAMSS）构建而成，NAMSS包含了数十年来在多个地区和地质条件下获得并公开的海上地震数据。 经过全面收集和过滤过程后，我们从122个调查区域中获得了2588份清洗和标准化后的地震剖面，涵盖了广泛的垂直和水平采样特性。为了确保可靠的实验结果，我们将该数据集进行了平衡处理，以保证任何单一调查都不会占据主导地位，并将其划分成互不重叠的宏区用于训练、验证和测试。这种区域分离的分割方式允许对模型在未见过地质和获取条件下的泛化能力进行稳健评估。 我们使用卷积和基于Transformer的模型通过定量分析及嵌入空间分析对该数据集进行了验证，结果显示Unicamp-NAMSS在不同地区之间以及各个内部都表现出显著的变化性，并且在整个采集宏区和调查类型上保持了连贯的结构。与广泛使用的解释数据集（Parihaka和F3区块）进行对比进一步表明，Unicamp-NAMSS覆盖了更广泛的地震外观空间部分，使其成为机器学习模型预训练的一个强有力候选者。 因此，该数据集为包括自我监督表征学习、迁移学习、基准测试如超分辨率或属性预测任务以及研究地震解释中的领域适应性在内的各种机器学习任务提供了一个宝贵的资源。
URL
https://arxiv.org/abs/2602.04890
