Evidence map›Paper›PMID 42655450›Full record

ArticleSensors (Basel, Switzerland)2026

Echo- and Image-Domain Fusion for Micro-Leak Detection and Localization in Subsea Gas Pipelines.

Haichao Liu, Jian Li, Xiaobin Jiang, Yi Luo

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Haichao LiuSchool of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.
Jian LiSchool of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.
Xiaobin JiangCNOOC (Tianjin) Pipeline Engineering Technology Co., Ltd., Tianjin 300450, China.
Yi LuoCNOOC (Tianjin) Pipeline Engineering Technology Co., Ltd., Tianjin 300450, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The detection of microleaks in subsea gas pipelines remains challenging in shallow-water environments because weak bubble-plume echoes are often obscured by seabed reverberation, ambient noise, and platform-induced interference. This study develops a custom multibeam forward-looking sonar system and a dual-domain framework for acoustic detection and localization of underwater gas microleakage. Local statistical enhancement suppresses stable background interference and strengthens anomalous bubble echoes. Blind deconvolution, adaptive grid-based thresholding, and spatial clustering then improve target sharpness, extract candidate bubble-plume regions, and reduce localized false detections. This unsupervised framework requires no pre-collected training data. It was evaluated in 30 independent sea-trial groups conducted in Bohai Bay using air to simulate leakage. Six orifice diameters from 0.5 to 3.0 mm were tested under different compressor-indicated pressures. The plume-observation distances (defined as the sonar-to-leak-device distance at the first confirmed plume response in the sonar image) ranged from 32 to 66 m across the tested conditions. Under the minimum tested condition of a 0.5 mm orifice and a compressor-indicated pressure of 0.5 MPa, the plume was observed at a distance of 32 m. The results demonstrate the feasibility of the proposed system for ROV-assisted detection and localization of subsea gas microleakage in low-visibility shallow-water environments.

Indexed as

acoustic imagingecho-image domain fusionmicro-leakage detection and localizationsubsea pipeline

Identifiers

PMID42655450
PMCPMC13517931

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.