A Novel Transfer Learning-Enhanced BiLSTM-DCNN Architecture for Mine Microseismic Signal Identification with Small Data Set

A Novel Transfer Learning-Enhanced BiLSTM-DCNN Architecture for Mine Microseismic Signal Identification with Small Data Set | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel Transfer Learning-Enhanced BiLSTM-DCNN Architecture for Mine Microseismic Signal Identification with Small Data Set Yong Zhao, Shihui Jiao, Tianhong Yang, Yao Wang, Haiyan Xu, Zhengdong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8704897/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The underground mining environment produces highly heterogeneous microseismic (MS) signals. Accurately identifying rock failure signals is crucial for source localization and failure mechanism analysis. However, data scarcity during initial monitoring stages severely limits recognition accuracy. Existing methods perform poorly under small-sample conditions, with low identification rates and weak generalization. To address this, this study proposes a Transfer-learning-enhanced Bidirectional LSTM and Deep Convolutional Network (Tr-BiLSTM-DCNN) model. It uses Mel-spectrograms for signal characterization, combining BiLSTM's bidirectional temporal modeling with DCNN's multi-scale spatial feature extraction to build a spatiotemporal feature representation. Training involves a two-phase strategy: pretraining and hyperparameter optimization on large cross-mine datasets, followed by domain-adaptive fine-tuning via transfer learning on the target mine's small-sample data. The model achieves 94.44% test accuracy under small-sample conditions, an 80.85% improvement over non-transfer baselines, and outperforms conventional CNN and LSTM methods. It provides an intelligent few-shot learning solution for mine MS monitoring, showing strong potential for engineering applications in dynamic disaster early warning. MS signal recognition Transfer learning BiLSTM Mel spectrum Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Mar, 2026 Reviews received at journal 16 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 26 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8704897","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589823009,"identity":"e7ffb641-57fe-4b67-9512-4aa90b06d18e","order_by":0,"name":"Yong Zhao","email":"","orcid":"","institution":"Northeastern University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zhao","suffix":""},{"id":589823010,"identity":"62aa60f6-65a6-425a-8be0-111767506a84","order_by":1,"name":"Shihui 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