Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning

Abhishek Venketeswaran; Nageswara Lalam; Ping Lu; Sandeep R Bukka; Michael P Buric; Ruishu Wright

doi:10.3390/s23136064

Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning

Sensors (Basel). 2023 Jun 30;23(13):6064. doi: 10.3390/s23136064.

Authors

Abhishek Venketeswaran¹, Nageswara Lalam^{1

2}, Ping Lu^{1

2}, Sandeep R Bukka^{1

2}, Michael P Buric³, Ruishu Wright¹

Affiliations

¹ National Energy Technology Laboratory, 626 Cochrans Mill Road, Pittsburgh, PA 15236, USA.
² NETL Research Support Contractor, 626 Cochrans Mill Road, Pittsburgh, PA 15236, USA.
³ National Energy Technology Laboratory, 3610 Collins Ferry Road, Morgantown, WV 26505, USA.

Abstract

This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is used to predict the Brillouin frequency shift (BFS) along the fiber and to assess its predictive uncertainty. We compare the predictions obtained from the proposed PML model with a conventional curve fitting method and evaluate the BFS uncertainty and data processing time for both methods. The proposed method is demonstrated using two BOTDA systems: (i) a BOTDA system with a 10 km sensing fiber and (ii) a vector BOTDA with a 25 km sensing fiber. The PML framework provides a pathway to enhance the VBOTDA system performance.

Keywords: data analytics; deep neural networks; distributed fiber sensors; optical fiber sensors; sensor data.

MeSH terms

Machine Learning*
Models, Statistical
Optical Devices*
Signal Processing, Computer-Assisted
Uncertainty

Grants and funding

This research was supported in part by appointments to the National Energy Technology Laboratory (NETL) Research Participation Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education. This technical effort was performed in support of the NETL’s ongoing research under Natural Gas Infrastructure (FWP Number: 1022424) and Grid Modernization Laboratory Consortium (GMLC, contract number:36149) projects.