High-fidelity spatiotemporal feature extraction for heterogeneous DAS signals
Disturbance event recognition in Distributed Acoustic Sensing (DAS) systems faces significant multi-scale heterogeneous challenges. Traditional fixed-length sliding windows often lead to morphological truncation or high data redundancy, severely destroying macroscopic topological semantics. This paper proposes a high-f...