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Distributed Acoustic Sensing – a new tool for seismic applications

Parker, Tom, Shatalin, Sergey, Farhadiroushan, Mahmoud·2014·First Break

Abstract

Distributed optical fibre sensors are established tools in the energy industry, finding many applications for production optimisation and integrity monitoring. Recently, a new class of instrument, the Distributed Acoustic Sensor (DAS), has been launched which adds seismic imaging to the list of energy industry applications. In this paper, we describe one such distributed acoustic sensor (named the iDAS) and demonstrate, through a series of lab experiments, the signal quality and performance that can be achieved. We show data which demonstrates the capability of the iDAS to measure the true acoustic signal (amplitude, frequency and phase) at all points along the sensing fibre length. We also compare the iDAS data with data collected from conventional point sensors and detail experiments which validate key performance criteria. We follow the lab experimental validation of the iDAS with a series of lab and field demonstrations. The lab demonstrations encompass localisation (ranging) of events away from the sensing fibre (for security applications) and acoustic imaging through the formation of a large acoustic camera using a single sensing fibre. The field demonstrations show comparisons of iDAS and geophone measurements in a surface seismic survey and improvements made by stacking shot records from an offshore VSP survey.

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Metadata

Authors
Parker, T., Shatalin, S., & Farhadiroushan, M.
Venue
First Break
Published
2014-02-01
Volume
32
Issue
2
ISSN
  • 0263-5046 (ISSN-L)
  • 1365-2397
OpenAlex
W2146625715
Cited by
372
References
0
FWCI
14.27
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Distributed Acoustic Sensing – a new tool for seismic applications. https://app.cochat.ai/papers/10.3997/1365-2397.2013034
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Parker, T., Shatalin, S., & Farhadiroushan, M. (2014). Distributed Acoustic Sensing – a new tool for seismic applications. First Break. https://doi.org/10.3997/1365-2397.2013034

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.