A novel transfer learning-enhanced BiLSTM-DCNN architecture for mine microseismic signal identification with small data set

Abstract The underground mining environment generates highly heterogeneous microseismic (MS) signals, whose accurate identification is crucial for source localization and failure mechanism analysis. Limited data in the early monitoring stages restrict recognition performance. Existing methods often...

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Autori principali:	Yong Zhao, Shihui Jiao, Tianhong Yang, Yao Wang, Haiyan Xu, Zhengdong Liu
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Springer 2026-05-01
Serie:	Geomechanics and Geophysics for Geo-Energy and Geo-Resources
Soggetti:	Microseismic signal recognition Transfer learning Bidirectional long short-term memory Mel spectrogram Small-sample learning
Accesso online:	https://doi.org/10.1007/s40948-026-01168-w
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