A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data

Martijn Van Den Ende*, Itzhak Lior, Jean Paul Ampuero, Anthony Sladen, Andre Ferrari, Cedric Richard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterization, and active source seismology. Using laser-pulse techniques, DAS turns (commercial) fiber-optic cables into seismic arrays with a spatial sampling density of the order of meters and a time sampling rate up to one thousand Hertz. The versatility of DAS enables dense instrumentation of traditionally inaccessible domains, such as urban, glaciated, and submarine environments. This in turn opens up novel applications such as traffic density monitoring and maritime vessel tracking. However, these new environments also introduce new challenges in handling various types of recorded noise, impeding the application of traditional data analysis workflows. In order to tackle the challenges posed by noise, new denoising techniques need to be explored that are tailored to DAS. In this work, we propose a Deep Learning approach that leverages the spatial density of DAS measurements to remove spatially incoherent noise with unknown characteristics. This approach is entirely self-supervised, so no noise-free ground truth is required, and it makes no assumptions regarding the noise characteristics other than that it is spatio-temporally incoherent. We apply our approach to both synthetic and real-world DAS data to demonstrate its excellent performance, even when the signals of interest are well below the noise level. Our proposed methods can be readily incorporated into conventional data processing workflows to facilitate subsequent seismological analyses.

Original languageAmerican English
Article number7
Pages (from-to)3371-3384
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number7
StatePublished - 1 Jul 2023

Bibliographical note

Publisher Copyright:
© 2012 IEEE.


  • Blind denoising
  • distributed acoustic sensing (DAS)
  • self-supervised deep learning
  • waveform coherence


Dive into the research topics of 'A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data'. Together they form a unique fingerprint.

Cite this