Distributed multichannel acoustic sensing (DMAS) enables large-scale sound event classification (SEC), but performance drops when many channels are degraded and when sensor layouts at test time differ from training layouts. We propose a learning-free, physics-informed inpainting frontend based on reverse time migration (RTM). In this approach, observed multichannel spectrograms are first back-propagated on a 3D grid using an analytic Green's function to form a scene-consistent image, and then forward-projected to reconstruct inpainted signals before log-mel feature extraction and Transformer-based classification. We evaluate the method on ESC-50 with 50 sensors and three layouts (circular, linear, right-angle), where per-channel SNRs are sampled from -30 to 0 dB. Compared with an AST baseline, scaling-sparsemax channel selection, and channel-swap augmentation, the proposed RTM frontend achieves the best or competitive accuracy across all layouts, improving accuracy by 13.1 points on the right-angle layout (from 9.7% to 22.8%). Correlation analyses show that spatial weights align more strongly with SNR than with channel--source distance, and that higher SNR--weight correlation corresponds to higher SEC accuracy. These results demonstrate that a reconstruct-then-project, physics-based preprocessing effectively complements learning-only methods for DMAS under layout-open configurations and severe channel degradation.

翻译：分布式多通道声学传感（DMAS）能够实现大规模声音事件分类（SEC），但当大量通道发生退化且测试时的传感器布局与训练布局不一致时，其性能会下降。我们提出一种基于逆时偏移（RTM）的无学习、物理信息修复前端。该方法首先利用解析格林函数将观测到的多通道频谱图在三维网格上进行反向传播，形成场景一致图像，随后通过前向投影重建修复后的信号，再进行对数梅尔特征提取和基于Transformer的分类。我们在ESC-50数据集上使用50个传感器和三种布局（圆形、线性、直角）进行评估，其中每通道信噪比（SNR）采样范围为-30至0 dB。与AST基线、缩放稀疏最大值通道选择以及通道交换增强方法相比，所提出的RTM前端在所有布局上均取得最佳或具有竞争力的准确率，在直角布局上将准确率提升了13.1个百分点（从9.7%提高到22.8%）。相关性分析表明，空间权重与信噪比的相关性强于通道-声源距离，且更高的信噪比-权重相关性对应更高的SEC准确率。这些结果证明，在布局开放配置和严重通道退化条件下，基于物理的“先重建后投影”预处理方法能有效补充纯学习方法在DMAS中的应用。