Exploring Research Trends in Distributed Acoustic Sensing with Machine Learning and Deep Learning: A Bibliometric Analysis of Themes and Emerging Topics

This paper explores the emerging research trends in Distributed Acoustic Sensing (DAS) with the integration of Machine Learning and Deep Learning technologies. DAS has diverse applications, including subsurface seismic monitoring, pipeline surveillance, and natural disaster detection. Using the Scopus database, 323 documents published between 2011 and 2023 were analysed. Through a comprehensive bibliometric analysis using the “bibliometrix” R package, the study aims to document the advancement in DAS techniques over the last decade, highlighting the publication patterns, key contributors, and frequently explored themes. The analysis reveals a steady increase in research output, with significant contributions from China and the United States. Core research areas identified include seismic monitoring, pipeline security, and infrastructure health monitoring. Additionally, the paper examines the impact of key publications, influential authors, and prolific research institutions. The findings provide valuable insights for both academic and industrial stakeholders, underscoring the potential for future innovations in DAS applications and helping to identify potential research gaps.

@article{Krishnan2025,
  title     = {Exploring Research Trends in Distributed Acoustic Sensing with Machine Learning and Deep Learning: A Bibliometric Analysis of Themes and Emerging Topics},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Nor Farisha Muhamad Krishnan and Jafreezal Jaafar},
  doi       = {10.14569/IJACSA.2025.0160518},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160518}
}