Evidence map›Paper›PMID 40549791›Full record

ArticlePloS one2025

High-precision deformation monitoring and intelligent early warning for wellbore based on BDS/GNSS.

Jiang Li, Lei Dai, Keke Xu, Xinyu Mei, Yifu Liu, Jianlin Shi, Hebing Zhang

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Jiang LiYong Coal Company Geological Survey Department, Henan Energy Group, Yongcheng, Henan, China.
Lei DaiYong Coal Company Geological Survey Department, Henan Energy Group, Yongcheng, Henan, China.
Keke XuSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, China.ORCID 0000-0002-0300-5179
Xinyu MeiSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, China.
Yifu LiuSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, China.
Jianlin ShiSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, China.
Hebing ZhangSchool of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To address the complex deformation of wellbores influenced by surrounding coal mining operations, this study employed an improved modified least-squares ambiguity decorrelation (MLAMBDA) algorithm based on the double-difference model for high-frequency dynamic computation of Bei Dou System and Global Navigation Satellite System (BDS/GNSS) observation data. A quantitative analysis was conducted on the performance of various combinations of BDS/GNSS in wellbore deformation monitoring, and the effects of different baseline lengths on the monitoring results were evaluated. Based on the high-precision deformation monitoring sequences, an intelligent early warning model for wellbore deformation was established using the deep learning Bi-LSTM algorithm. The results indicate that the monitoring accuracy of the BDS/GNSS multi-system combination in the E, N and U directions is within 2 mm, with all three directions outperforming the results obtained from a single Global Position System (GPS) system. As the baseline length increased from 1 km to 6 km, the accuracy in the E, N, and U directions decreased by 15.8%, 16.0%, and 5.6%, respectively. Within a 6 km range, the horizontal accuracy remains better than 3 mm, while the vertical accuracy is better than 6 mm, meeting the requirements for wellbore deformation monitoring. The early warning model can flexibly adapt to the deformation conditions at different sites and the various disturbances encountered, effectively capturing the complex nonlinear time-varying characteristics of the observation time series. The prediction of future results for one month based on one year of observation sequences achieves an accuracy better than mm, providing a safeguard for safe production in mines. This research method can also be extended to use BDS/GNSS for hourly level high-precision deformation monitoring and early warning of major engineering infrastructure such as bridges, dams, and high-speed railway systems.

Indexed as

Coal MiningGeographic Information SystemsAlgorithmsDeep Learning

Identifiers

PMID40549791
PMCPMC12184896

What OpenQuestion holds

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LicenceCC BY
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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.