Three-Component Distributed Acoustic/Seismic Sensing

Fiber-optic distributed acoustic sensing (DAS) has become a revolutionary technology in the field of acoustic/seismic wave detection as it has disruptive advantages over point acoustic/seismic sensors. Especially, DAS has found vital applications in oil/gas exploration and development. However, three-component DAS (3C-DAS) remains an unsolved bottleneck problem as the present DAS is only sensitive to the axial strain, limiting the application range of DAS greatly. Here, we proposed and verified the 3C-DAS concept based on the theory proposed. We processed signals from three independent optical fibers within a specially designed dual-sine-structured sensing cable to realize 3C-DAS. Field trials of the 3C-DAS units were completed outdoor and in the real oilfield, respectively, by using an ultra-sensitive DAS instrument developed. Furthermore, a 100 m-long 3C-DAS cable was designed, fabricated, and tested. The experimental results indicate that 3C-DAS has the statistically comparable signal-to-noise ratio and better imaging resolution due to higher spatial density when compared to electronic 3C geophones. This work pioneers the study of 3C-DAS and paves a way to develop a new generation of vector DAS systems, including 3C-DAS-based geophones, hydrophones, and sonars for imaging geophysical structures with higher fineness, detecting undersea targets with better resolution, and positioning aerial vehicles with higher accuracy.

Authors

Institutions

Publication Details

Journal
Photonic Sensors
Published
2026-02-09
DOI
https://doi.org/10.26599/phos.2026.9560001
Primary Topic
Seismic Waves and Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Three-Component Distributed Acoustic/Seismic Sensing

Shujie An, Shengtao Zou, Junhang Wang, Tianyuan Yang et al.
Photonic Sensors
Seismic Waves and Analysis
article

Three-Component Distributed Acoustic/Seismic Sensing

Shujie An, Shengtao Zou, Junhang Wang, Tianyuan Yang, Lingfeng Meng, Haolin Chen, Yunjiang Rao, Qiang Jing, Zhaoyu Li, Yong SUN, Zengling RAN, Fengyu TAN, Sidan YU, Gang YU, Qiyun ZHANG, Sijin ZHENG, Rong TANG
article en

Abstract

Fiber-optic distributed acoustic sensing (DAS) has become a revolutionary technology in the field of acoustic/seismic wave detection as it has disruptive advantages over point acoustic/seismic sensors. Especially, DAS has found vital applications in oil/gas exploration and development. However, three-component DAS (3C-DAS) remains an unsolved bottleneck problem as the present DAS is only sensitive to the axial strain, limiting the application range of DAS greatly. Here, we proposed and verified the 3C-DAS concept based on the theory proposed. We processed signals from three independent optical fibers within a specially designed dual-sine-structured sensing cable to realize 3C-DAS. Field trials of the 3C-DAS units were completed outdoor and in the real oilfield, respectively, by using an ultra-sensitive DAS instrument developed. Furthermore, a 100 m-long 3C-DAS cable was designed, fabricated, and tested. The experimental results indicate that 3C-DAS has the statistically comparable signal-to-noise ratio and better imaging resolution due to higher spatial density when compared to electronic 3C geophones. This work pioneers the study of 3C-DAS and paves a way to develop a new generation of vector DAS systems, including 3C-DAS-based geophones, hydrophones, and sonars for imaging geophysical structures with higher fineness, detecting undersea targets with better resolution, and positioning aerial vehicles with higher accuracy.

Photonic SensorsVol. 16(1)
Beijing Institute of Petrochemical Technology (CN), University of Electronic Science and Technology of China (CN), Chengdu Surveying Geotechnical Research Institute (CN), Heinz Optical Engineering (United States) (US)
National Natural Science Foundation of China
Openalex Percentile: Top 72%
Seismic Waves and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.