Scholarlink

<p>Distributed Acoustic Sensing (DAS) has shown promise for real-time monitoring of large-scale infrastructure by providing spatio-temporal information about vibrations along a fiber optic cable. However, data easily reaches into terabytes per day due to high spatial resolution and acquisition frequency, making storage, transfer, and analysis economically infeasible especially for applications requiring real-time decisions. Tensor Networks (TNs) are a data structure well poised to address the challenges of DAS as they are effective at capturing signals in low rank and enable linear operations (e.g. signal processing) in the compressed space, providing computational savings and bypassing the need to decompress. This article is the first to demonstrate how TNs can be applied to DAS data and recreates a pre-existing workflow for DAS in TN format for experimental data from a field-scale wellbore. The methods achieved <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\sim 40x-60x\)</EquationSource> </InlineEquation> real-time compression on a laptop with high accuracy and efficiently processed the data in the compressed space without needing to prematurely decompress. Not only does this research help reduce the cost of implementing DAS technology, but it creates new opportunities between the fields of signal processing and TNs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Quantum-inspired workflow for processing distributed fiber-optic sensor data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

查看全文
  • Hayden Gemeinhardt,
  • Jyotsna Sharma,
  • Michael Kastoryano

摘要

Distributed Acoustic Sensing (DAS) has shown promise for real-time monitoring of large-scale infrastructure by providing spatio-temporal information about vibrations along a fiber optic cable. However, data easily reaches into terabytes per day due to high spatial resolution and acquisition frequency, making storage, transfer, and analysis economically infeasible especially for applications requiring real-time decisions. Tensor Networks (TNs) are a data structure well poised to address the challenges of DAS as they are effective at capturing signals in low rank and enable linear operations (e.g. signal processing) in the compressed space, providing computational savings and bypassing the need to decompress. This article is the first to demonstrate how TNs can be applied to DAS data and recreates a pre-existing workflow for DAS in TN format for experimental data from a field-scale wellbore. The methods achieved \(\sim 40x-60x\) real-time compression on a laptop with high accuracy and efficiently processed the data in the compressed space without needing to prematurely decompress. Not only does this research help reduce the cost of implementing DAS technology, but it creates new opportunities between the fields of signal processing and TNs.