当大数据成为诅咒：空间异质性与被动声学监测数据学习的局限性
When Big Data Becomes a Curse: Spatial Heterogeneity and the Limits of Learning from Passive Acoustic Monitoring Data
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中文总结 AI 辅助
本研究通过分析被动声学监测大数据，发现随机窗口验证高估模型对未见站点的迁移性能，提出依赖感知验证和独立空间采样以应对空间异质性挑战。
中文摘要 AI 辅助
被动声学监测产生大量档案，其录音按部署、季节、站点标识符和采集配置进行聚类。我们分析了来自38次部署、20个大西洋加拿大站点标识符和21个接收器位置的908,072个AIS标记的679秒录音。AIS接触先验变化超过200倍，每次部署的筛查分布需要局部解释。在两个季节均观测到的18个站点标识符上的双向跨季节迁移预测站点身份高于5.56%的均匀机会水平，AIS接触录音的平衡准确率为15.9%，无AIS接触录音为16.4%。相同的描述符分别以74.1%和85.6%的平衡准确率预测两种水听器模型，但水听器模型与季节及其他部署级采集差异强烈混淆。在回顾性筛查并封顶的54,507个录音基准上，重复站点分组留出法产生ROC-AUC为0.612，站点自助法95%区间为0.582至0.648，而随机窗口诊断法为0.661。它们的配对差异为0.049（0.040至0.056）。移除原始能量将未见站点的ROC-AUC从0.612降至0.603，而探索性训练站点规模分析是非单调的。这些结果表明，随机窗口验证高估了该语料库中对未见站点标识符的迁移。它们支持依赖感知验证和更广泛的独立空间采样，而空间专家模型仍是一个假设而非既定解决方案。
英文摘要
Passive Acoustic Monitoring produces large archives whose recordings are clustered by deployment, season, station identifier, and acquisition configuration. We analyze 908,072 AIS-labeled 679-second recordings from 38 deployments, 20 Atlantic Canadian station identifiers, and 21 receiver positions. The AIS-contact prior varies by more than 200-fold, and per-deployment screening distributions require local interpretation. Bidirectional cross-season transfer over the 18 station identifiers observed in both seasons predicts station identity above the 5.56% uniform-chance level, with balanced accuracy of 15.9% for AIS-contact and 16.4% for no-AIS-contact recordings. The same descriptors predict the two hydrophone models at 74.1% and 85.6% balanced accuracy, respectively, but hydrophone model is strongly confounded with season and other deployment-level acquisition differences. On a retrospectively screened and capped benchmark of 54m507 recordings, repeated station-grouped holdout yields an ROC-AUC of 0.612 with a station-bootstrap 95% interval of 0.582 to 0.648, compared with 0.661 under a random-window diagnostic. Their paired difference is 0.049 (0.040 to 0.056). Removing raw energy changes unseen-station ROC-AUC from 0.612 to 0.603, while an exploratory training-station scale analysis is non-monotonic. These results show that random-window validation overstates transfer to unseen station identifiers in this corpus. They support dependence-aware validation and broader independent spatial sampling, while spatial-expert models remain a hypothesis rather than an established remedy.
