区分传感器失效与低信噪比两种鲁棒性，发现训练方式比模型结构更重要。
Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data
- 通过真实多源数据构建统一检测基准，分离不同故障模式
- 低信噪比下新模型AUC达0.939，传统模型仅0.532-0.572
- 训练时的传感器丢弃才是鲁棒性的关键，冗余结构作用较小
可靠事件检测对二氧化碳捕集与封存（CCS）、地热开发及工业状态监测至关重要。在这些场景中，检测器需同时应对传感器失效和信号淹没于噪声的情况。然而，这两种失效模式常被混淆，复杂架构常被错误归因于鲁棒性。本文整合三个物理来源的真实数据——Hi-net地震波形、犹他州FORGE 2024井下分布式声学传感（DAS）数据、MAFAULDA工业振动数据——统一映射为8通道、256样本的表示形式，构建统一二分类事件检测基准。评估了在逐样本传感器丢弃训练下的容错检测器CEPHALON，与三种标准模型（1D卷积网络、时间卷积网络、紧凑型Transformer）在相同训练方案下的表现。在干净数据上所有模型均接近完美（AUC ~ 0.99）。在逐步传感器丢失下，简单模型已具鲁棒性，CEPHALON无优势；但在加性噪声下，CEPHALON显著更稳健：-2.5 dB时整体AUC为0.939，而卷积基线仅为0.532–0.572。同架构消融实验表明：推理时关闭内部冗余仅小幅降低低信噪比性能，而移除传感器丢弃训练则导致性能崩溃（-5 dB时从0.899降至0.603）。因此，训练方式是主导因素，冗余结构仅为次要因素。论文提供完整可复现流水线，所有图表均可再生。
原文摘要 · Abstract (English)
Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal is buried in noise. These two failure modes are routinely conflated, and architectural complexity is often credited with robustness it may not deserve. We assemble a unified binary event-detection benchmark from three physically distinct real sources -- Hi-net seismic waveforms, Utah FORGE 2024 borehole DAS, and MAFAULDA industrial vibration -- each mapped to a common 8-channel, 256-sample representation, and evaluate a fault-tolerant detector (CEPHALON) trained with per-sample sensor-dropout against standard detectors (a 1D convolutional network, a temporal convolutional network, and a compact Transformer) trained with an identical recipe. On clean data every model is near-perfect (AUC ~ 0.99). Under progressive sensor loss, simple models with sensor-dropout are already robust and CEPHALON holds no advantage. Under additive noise, however, CEPHALON degrades far more gracefully: at -2.5 dB its overall AUC is 0.939 versus 0.532-0.572 for the convolutional baselines. Same-architecture ablations isolate the cause: disabling internal redundancy at inference reduces the low-SNR advantage only modestly, whereas removing sensor-dropout training collapses it (0.899 to 0.603 at -5 dB). The training recipe is therefore the dominant cause and parallel redundancy only secondary. We release a complete, numbered, reproducible pipeline so that every figure can be regenerated.
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