AI 中文总结
研究针对长时间被动声学部署录音与船只轨迹等未关联问题，提出数据库原生工作流程，将水听器录音与 AIS 位置报告对齐生成距离解析数据，经处理大量数据生成结构化表，实现地理人工智能框架，支持相关海洋环境分析建模。
AI 中文摘要
长时间的被动声学部署会产生大量未与船只轨迹或相遇结构相关联的录音档案，使得距离和接触条件作为变量不可用，需要人工选择进行分析。为解决此限制，我们提出一种数据库原生工作流程，将水听器录音与自动识别系统（AIS）位置报告对齐以生成距离解析数据。固定时长的录音窗口和 AIS 消息存储为持久地理空间表，并通过索引时空连接关联，取代内存中的嵌套迭代。在本研究中，该方法处理约 9.5×10⁵个录音窗口和 6.9×10⁶个 AIS 位置报告，生成结构化表，还能直接计算最近接近点并通过确定性频谱排序表征背景条件。这种方式实现了一个地理人工智能框架，可查询数据能直接用于机器学习。结果数据产品揭示主要是噪声主导的情况，船只贡献主要出现在较短距离。频谱图和定量分析表明噪声中嵌入微弱音调特征且信噪比随距离一致衰减，支持在实际海洋环境中用于可扩展机器学习、相似性分析和预测声学建模。
英文摘要
Long-duration passive acoustic deployments produce large archives of recordings that are not linked to vessel tracks or encounter structure, leaving range and contact conditions unavailable as variables and requiring manual selection for analysis. To address this limitation, we propose a database-native workflow that aligns hydrophone recordings with Automatic Identification System (AIS) position reports to produce distance-resolved data. Fixed-duration recording windows and AIS messages are stored as persistent geospatial tables and associated through an indexed spatiotemporal join, replacing in-memory nested iteration with a single scalable set-based database process capable of handling continuous, multi-year, million-window archival deployments without exhausting available memory. In this study, the approach processes approximately 9.5x10e5 recording windows and 6.9x10e6 AIS position reports, producing a structured table that separates no-contact, single-contact, and two-contact windows, with the closest point of approach computed directly where applicable and background conditions characterized via deterministic spectral ranking. This formulation enables a GeoAI framework in which spatially indexed, queryable data become directly usable for machine learning. The resulting data product reveals predominantly noise-dominated conditions, with vessel contributions emerging mainly at shorter ranges, indicating that the task lies in extracting structure under background-limited regimes. Spectrogram and quantitative analyses show weak tonal signatures embedded in noise and a consistent decay of signal-to-noise ratio with distance, supporting the use of this representation for scalable machine learning, similarity analysis, and predictive acoustic modelling in real maritime environments.
CommentsOCEANS'26 - Monterey
