Research on CR-based offset technique for mining deformation monitoring

AbstractRapid estimation of the coseismic fault model for medium-to-large-sized earthquakes is key for disaster response. To estimate the coseismic fault model for large earthquakes, the Geospatial Information Authority of Japan and Tohoku University have jointly developed a real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD can estimate the single rectangular fault model and slip distribution along the assumed plate interface. The single rectangular fault model is useful as a first-order approximation of a medium-to-large earthquake. However, in its estimation, it is difficult to obtain accurate results for model parameters due to the strong effect of initial values. To solve this problem, this study proposes a new method to estimate the coseismic fault model and model uncertainties in real time based on the Bayesian inversion approach using the Markov Chain Monte Carlo (MCMC) method. The MCMC approach is computationally expensive and hyperparameters should be defined in advance via trial and error. The sampling efficiency was improved using a parallel tempering method, and an automatic definition method for hyperparameters was developed for real-time use. The calculation time was within 30 s for 1 × 106 samples using a typical single LINUX server, which can implement real-time analysis, similar to REGARD. The reliability of the developed method was evaluated using data from recent earthquakes (2016 Kumamoto and 2019 Yamagata-Oki earthquakes). Simulations of the earthquakes in the Sea of Japan were also conducted exhaustively. The results showed an advantage over the maximum likelihood approach with a priori information, which has initial value dependence in nonlinear problems. In terms of application to data with a small signal-to-noise ratio, the results suggest the possibility of using several conjugate fault models. There is a tradeoff between the fault area and slip amount, especially for offshore earthquakes, which means that quantification of the uncertainty enables us to evaluate the reliability of the fault model estimation results in real time.

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An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

Remote Sensing ◽

10.3390/rs13122259 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2259

Author(s):

Ruicheng Zhang ◽

Chengfa Gao ◽

Qing Zhao ◽

Zihan Peng ◽

Rui Shang

Keyword(s):

Real Time ◽

Sampling Rate ◽

Threshold Value ◽

Deformation Monitoring ◽

Success Rates ◽

Multipath Mitigation ◽

Mode Decomposition ◽

Observation Noise ◽

Original Observation ◽

Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

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Vibration Deformation Monitoring of Offshore Wind Turbines Based on GBIR

Journal of Ocean University of China ◽

10.1007/s11802-021-4673-8 ◽

2021 ◽

Vol 20 (3) ◽

pp. 501-511

Author(s):

Deming Ma ◽

Yongsheng Li ◽

Yanxiong Liu ◽

Jianwei Cai ◽

Rui Zhao

Keyword(s):

Wind Turbines ◽

Deformation Monitoring ◽

Offshore Wind ◽

Offshore Wind Turbines

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A New Approach for Cylindrical Steel Structure Deformation Monitoring by Dense Point Clouds

Remote Sensing ◽

10.3390/rs13122263 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2263

Author(s):

Dongfeng Jia ◽

Weiping Zhang ◽

Yuhao Wang ◽

Yanping Liu

Keyword(s):

Steel Structure ◽

Steel Structures ◽

Point Clouds ◽

Deformation Monitoring ◽

Deformation Analysis ◽

Relative Deformation ◽

Structure Deformation ◽

Single Time ◽

Cylindrical Steel ◽

Cylinder Fitting

As fundamental load-bearing parts, the cylindrical steel structures of transmission towers relate to the stability of the main structures in terms of topological relation and performance. Therefore, the periodic monitoring of a cylindrical steel structure is necessary to maintain the safety and stability of existing structures in energy transmission. Most studies on deformation analysis are still focused on the process of identifying discrepancies in the state of a structure by observing it at different times, yet relative deformation analysis based on the data acquired in single time has not been investigated effectively. In this study, the piecewise cylinder fitting method is presented to fit the point clouds collected at a single time to compute the relative inclination of a cylindrical steel structure. The standard deviation is adopted as a measure to evaluate the degree of structure deformation. Meanwhile, the inclination rate of each section is compared with the conventional method on the basis of the piecewise cylinder fitting parameters. The validity and accuracy of the algorithm are verified by real transmission tower point cloud data. Experimental results show that the piecewise cylinder fitting algorithm proposed in this research can meet the accuracy requirements of cylindrical steel structure deformation analysis and has high application value in the field of structure deformation monitoring.

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Editorial to the special edition of the JAG on Deformation Monitoring

Journal of Applied Geodesy ◽

10.1515/jag-2020-0018 ◽

2020 ◽

Vol 14 (2) ◽

pp. 119

Author(s):

V. Gikas ◽

G. Retscher ◽

H. Kahmen ◽

H. Neuner ◽

C. Rizos

Keyword(s):

Deformation Monitoring ◽

Special Edition

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A novel GNSS deformation feature extraction method based on ensemble improved LMD threshold denoising

Journal of Applied Geodesy ◽

10.1515/jag-2020-0039 ◽

2020 ◽

Vol 14 (4) ◽

pp. 445-453

Author(s):

Qian Fan ◽

Yiqun Zhu

Keyword(s):

Feature Extraction ◽

Spline Interpolation ◽

Simulated Data ◽

Mining Area ◽

Deformation Monitoring ◽

Denoising Method ◽

Feature Extraction Method ◽

Deformation Feature ◽

Mode Decomposition ◽

Better Than

AbstractIn order to solve the problem that the moving span of basic local mean decomposition (LMD) method is difficult to choose reasonably, an improved LMD method (ILMD), which uses three cubic spline interpolation to replace the sliding average, is proposed. On this basis, with the help of noise aided calculation, an ensemble improved LMD method (EILMD) is proposed to effectively solve the modal aliasing problem in original LMD. On the basis of using EILMD to effectively decompose the data of GNSS deformation monitoring series, GNSS deformation feature extraction model based on EILMD threshold denoising is given by means of wavelet soft threshold processing mode and threshold setting method in empirical mode decomposition denoising. Through the analysis of simulated data and the actual GNSS monitoring data in the mining area, the results show that denoising effect of the proposed method is better than EILMD, ILMD and LMD direct coercive denoising methods. It is also better than wavelet analysis denoising method, and has good adaptability. This fully demonstrates the feasibility and effectiveness of the proposed method in GNSS feature extraction.

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Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

Remote Sensing ◽

10.3390/rs13153044 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3044

Author(s):

Mingjie Liao ◽

Rui Zhang ◽

Jichao Lv ◽

Bin Yu ◽

Jiatai Pang ◽

...

Keyword(s):

Time Series ◽

Large Scale ◽

Ground Deformation ◽

Dynamic Change ◽

Deformation Monitoring ◽

Chinese Loess Plateau ◽

Ground Subsidence ◽

Shanxi Province ◽

Environment Change ◽

Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

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