将声学录音与船舶定位数据对齐，构建可查询的海量海洋声学数据库。
From Continuous Deployment to Queryable Dataset: Terabyte-Scale AIS-Aligned Passive Acoustic Labelling
- 用时空索引将声学片段与AIS位置数据关联，避免内存瓶颈。
- 处理95万段录音和690万条AIS数据，实现接触状态分类与最近接近点计算。
- 支持机器学习的可查询数据结构，适合海洋声学分析与模型训练。
长时间被动声学监测产生大量未关联船只轨迹或相遇结构的录音，导致距离与接触条件缺失，需人工筛选分析。为解决此问题，我们提出一种基于数据库的工作流，将水听器录音与自动识别系统（AIS）位置报告对齐，生成具有距离分辨能力的数据。固定时长的录音窗口与AIS消息以持久化地理空间表形式存储，并通过索引化的时空连接关联，替代传统的内存嵌套迭代，实现无需耗尽内存即可处理连续、多年、百万级窗口的长期档案部署。本研究处理约9.5×10⁵个录音窗口和6.9×10⁶条AIS位置报告，生成结构化表格，区分无接触、单接触与双接触窗口，可在适用情况下直接计算最近接近点，并通过确定性频谱排序刻画背景条件。该方法构建了可用于机器学习的地理空间索引化可查询数据框架。结果表明，环境以噪声为主导，船舶信号主要在短距离显现，凸显在背景限制下提取结构的挑战。光谱图与定量分析显示噪声中存在微弱谐波特征，且信噪比随距离一致衰减，验证了该表示在真实海况下适用于大规模机器学习、相似性分析与预测建模。
原文摘要 · Abstract (English)
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.
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