Application of PS-INSAR Technique on Health Diagnosis of the Deformable Body on Front Slope beside Mountain Tunnel Portal

Jia, Jiaxin

doi:https://doi.org/10.1155/2020/8823382

Mathematical Problems in Engineering

On this page

Abstract Introduction Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Special Issue

Artificial Intelligence for Civil Engineering

View this Special Issue

Research Article | Open Access

Volume 2020 | Article ID 8823382 | https://doi.org/10.1155/2020/8823382

Application of PS-INSAR Technique on Health Diagnosis of the Deformable Body on Front Slope beside Mountain Tunnel Portal

Jiaxin Jia¹

Academic Editor: Zheng-zheng Wang

Received18 Sept 2020

Revised10 Oct 2020

Accepted04 Nov 2020

Published29 Nov 2020

Abstract

When tunnel construction develops to surrounding mountainous areas, tunnel portal presents the topographic characteristics of “high, steep, and slant” and geological characteristics of kinds of hugely thick colluvial or residual slope, and the health condition of the deformable body on front slope beside tunnel portal has a great influence on tunnel construction and operation. Tunnel portal has suffered from many disasters, and it is urgent to study and solve the engineering problem on how to diagnose the health condition of front slope beside tunnel portal quickly and accurately. Based on the tunnel of Jianquan village, this paper uses PS-INSAR technique for the first time to analyze and diagnose the health condition of the deformable body on front slope beside mountain tunnel portal at the construction stage and operation stage, so as to provide early warnings to unstable deformable body at tunnel portal and provide strong bases for the treatment of unstable deformable body. Therefore, PS-INSAR technique promotes the health monitoring method for deformable body on front slope beside mountain tunnel portal to a higher level.

1. Introduction

Geological problems of tunnel portal primarily include weak foundation of tunnel portal, severe bias of tunnel portal, hugely thick overburden of tunnel portal, and instability of front slope beside tunnel portal [1]. Early detection and early treatment are basic principles for the treatment of surface deformation and even landslide on front slope beside mountain tunnel portal [2]. There are two main types of health monitoring method for deformable body on front slope beside mountain tunnel portal: the first type is adopting a variety of sensor monitoring means on the ground, e.g., vertical slant monitoring hole [3], external vertical monitoring pier, vertical displacement monitoring, multipoint displacement meter, and GNSS, to monitor internal deformation, external deformation, and seepage pressure of cracking body and stress of supporting body, etc. [4, 5]. These means have advantages of high precision and reliable results, and their disadvantages are spending too much manpower and material resources, high cost, sparse measuring points, long interval before repetition measuring, cannot effectively guaranteeing the safety of monitoring personnel, small monitoring scope, and severe environment influence [6]. Besides, most side slopes of highways locate in remote mountains and canyons, employees have large fluidity and uneven professional quality, and they are prone to have strong thinking inertia and fluky mind. Their safety awareness is weak, and then the accuracy and timeliness of monitoring data cannot be guaranteed due to measurement difficulty and serious influence of human factors [7]. The second type is adopting traditional surface deformation monitoring means such as GPS which have high precision, but they have low spatial density and cannot provide surface deformation information of the whole area [8, 9].

Differential interferometric synthetic aperture radar technique (INSAR technique) is a kind of space-to-earth observation technique [10]. In recent years, INSAR technique is in wide application stage of business, such as surface settlement observation and analysis of the southwest of Tianjin based on PS-INSAR technique, landslide seismic damage observation and analysis of Zhangmu Port of Tibet based on INSAR technique, geological disaster monitoring based on applying INSAR technique in Beidou satellite [11], and the application of INSAR technique in research of western coal mine goaf of Guizhou [12]. At present, INSAR technique has also made great progress in the field of surface deformation monitoring on highways. For example, PS-INSAR technique was used to extract the settlement speed figure and settlement profile along Beijing-Shanghai highway and successfully identified 9 settlement centers along the highway [13]. And the section of Kunmo highway in Yunnan from Kunming to Yuxi was selected, and a “general survey” of disasters along the highway was conducted and then put forward suggestions for reasonable monitoring of the highway according to test results. However, the application of INSAR technique was still lacking in the field of tunnel and underground engineering construction. SBAS-INSAR technique was used to select scene 10 sentinel-1A data covering the engineering scope to conduct time sequence settlement monitoring on the tunnel engineering site [14], and settlement monitoring results of the engineering site was obtained. Then, the monitoring accuracy was evaluated and verified by actual leveling data [15]. Time sequence INSAR technique was adopted to monitor surface settlement along Beijing subway network. And taking Taoping tunnel of Houyue line as an example, INSAR technique was adopted for surface deformation monitoring investigation research of deep coal mine goaf in railway tunnel damage [16]. Above research studies primarily applied INSAR technique in the deformation monitoring of railway tunnel site [17], but as a millimeter level technique having higher reliability than traditional INSAR technique, PS-INSAR technique has not been applied in health diagnosis of the deformable body on front slope beside mountain tunnel portal [18, 19].

Based on the tunnel of Jianquan village, this paper uses PS-INSAR technique for the first time to analyze and diagnose the health condition of the deformable body on front slope beside mountain tunnel portal [20, 21]. Through the inversion and long-term dynamic tracking of historical deformation of the deformable body on front slope beside tunnel portal, understanding and awareness of the slope deformation law in monitoring area are improved [22], thus providing a more scientific basis and reference for the early warning and treatment of the deformation of the deformable body on front slope beside tunnel portal.

2. General Situation

2.1. Characteristics of INSAR Technique

PS-INSAR technique is to use two synthetic aperture radar antennas with interference imaging ability (or use an antenna to repeat observation), to obtain two coherent single-view complex images from the same area with a certain angle of view [23], obtain surface elevation information by interference phase information, and then reconstruct the digital surface elevation model [24]. It is characterized by all-day and all-weather Earth observation without restrictions of light and climate conditions, and even the information covered by it can be obtained through the surface or vegetation [25]. The primary applications of this technique are to produce digital elevation models and to monitor the vertical direction of small displacements or deformations [26].

2.2. Basic Information of Front Slope beside the Portal of Jianquan Village Tunnel

The tunnel is located on the right bank of Hanyuan Lake in Hanyuan County, Ya'an. The highest elevation is over 1200 m, the lowest elevation is about 800 m above the river bed of Dadu River, and the relative height difference is up to 400m. It belongs to Zhongshan landform. The tunnel is laid out in SE∼NW direction, the numbers of starting pile and ending pile are K118 + 222∼K119 + 664, and the length is 1442 m.

The surrounding rock at the entrance of tunnel is primarily composed of colluvial deposits and silty mudstones, and the colluvial deposits are primarily composed of granular structure. The rock mass at the exit of tunnel is primarily silty mudstone, carbonaceous shale, and sandstone, which is soft rock and medium soft rock. The exit section (K119 + 700) develops a toppling deformable body, and the surface occurrence of the deformable body is 143°∠32°. Because of the steep dip angle of surface rock mass, it is pulled by gravity to topple outwards. The distribution elevation of the deformable body is 914∼1050 m, and the lowest point is 23 m away from the tunnel roof.

3. The Application of PS-INSAR Technique in Diagnosing Deformable Body on Front Slope beside Tunnel Portal

3.1. Technical Parameter Selection

3.1.1. SAR Data Selection

Sentinel-1 is a two-satellite Earth observation satellite operated by the European Space Agency's Copernicus Project (GMES) [27]. It consists of two satellites, c-band synthetic aperture radar (frequency: 5.4 GHz), with a 12-day revisit cycle. Sentinel-1 satellite SAR data is used to obtain the distribution map in the operation area (shown in Figure 1). Inside it, IW shooting mode can obtain SAR image data with a resolution of 5 m × 20 m and a width of 250 km. Here, scene 68 data from 2018 to 2020 were selected.

3.1.2. SAR Data Processing

Key steps of INSAR interferometric data processing process were (1) selection of public primary image, (2) image registration, (3) differential interference processing, (4) target extraction of point CS, (5) INSAR phase unwrapping, (6) linear deformation phase and residual elevation phase calculation, and (7) estimation and removal of error and atmospheric effect.

Among them, the basic principle of differential interference processing was that, after scene 68 image registration, all auxiliary images were resampled to primary images space. In frequency domain, public frequency band of primary and auxiliary images was prefiltered to generate the filtered primary and auxiliary images. After resampling, all auxiliary images were multiplied by the conjugate of the main image to generate interference phase diagram of images. Obtained phase information included the information of surface deformation, surface terrain phase, and so on. Meanwhile, the data of external digital elevation model (DEM) was used to simulate terrain phase, and the terrain phase of interference graph generated in this step was removed to generate differential interference graph. At this point, the phase of the X-th scatterer point (PS) of the ith differential interference graph was obtained as follows:where is the deformation phase in view direction; is the atmospheric influence phase; is the orbit error phase; is the residual terrain phase due to DEM error; and is the noise phase.

3.2. Deformation Monitoring Calculation Results

3.2.1. Deformation Monitoring Calculation Results of Front Slope beside Tunnel Portal

Deformation speeds distribution in the whole research area is shown in Figure 2. 70% of them were between −10 and + 10 mm/year. Overall, the deformation speeds ranged from −26.277 to + 15.42 mm/year.

3.2.2. Deformation Monitoring Calculation Results of the Entrance Section of Jianquan Village Tunnel

According to deformation monitoring results of the entrance section of Jianquan village tunnel shown in Figure 3, the location was 29.3603388°N and 102.67806388°E. The deformation value of point TS1 continued to decrease from October 2018 to October 2019 and is relatively stable recently. Its accumulative settlement maximum in two years was about 30 mm, and the annual settlement speed was 12.628 mm/year. Point TS2 continued to settle from April 2018 to April 2020 (Figure 4). The cumulative settlement maximum in two years was about 25 mm, and the annual settlement speed was 11.348 mm/year.

(a)

(b)

3.2.3. Deformation Monitoring Calculation Results of the Exit Section of Jianquan Village Tunnel

According to deformation monitoring results of the exit section of Jianquan village tunnel shown in Figure 5, the location was 29.362805°N and 102.677111°E. The deformation values continued to decrease from October 2018 to April 2020. The cumulative settlement maximum of point TS3 in two years was more than 35 mm, and the annual settlement speed was 17.136 mm/year. The cumulative settlement maximum of point TS4 in two years was about 50 mm, and the annual settlement speed was 19.125 mm/year.

3.3. Analysis of Deformation Monitoring Results

By using PS-INSAR technique to conduct quantitative analysis on the surface deformation of front slope beside Jianquan village tunnel portal in Emeishan-Hanyuan highway, the following results are obtained: through precise analysis on the surface deformation speed of Jianquan village tunnel in Emeishan-Hanyuan highway from January 2018 to April 2020, as exhibited in Figure 6, it is found that the exit section (K119 + 700) of Jianquan village develops a deformable body, which is consistent with original investigation results. The annual deformation speed maximum of this deformable body is 19.125 mm/year, the annual deformation speed of section K118 + 400 is 11.348 mm/year, and this quantitative analysis of the change of the deformable body has important value to guide the treatment of the deformable body on front slope beside tunnel portal.

(a)

(b)

4. Conclusions

By using PS-INSAR technique to conduct research and analysis on the surface deformation law of front slope beside tunnel portal, the development state of the deformable body is quantitatively grasped, and the parameters (position, deformation speed, accumulative deformation, etc.) of the deformable body on front slope beside tunnel portal are obtained. Therefore, health condition of the bad deformable body is diagnosed. Then, the “bull's-eye” advantage is taken to detect early and treat early, which can attain the effect of rapid warning and precise management, reduce the safety risk during periods of tunnel construction and operation, and reduce deformation treatment cost.

Data Availability

The data are mainly composed of graphs and tables in the paper and can be referenced.

Conflicts of Interest

The author declares no conflicts of interest.

Acknowledgments

This research was supported by the Miaozi Engineering Key Project of the Science and Technology Department of Sichuan Province (grant no. 2020JDRC0078) and the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (China) Open Fund (grant no. SKLGP2020K018).

References

W.-F. Chen, H.-L. Gong, B.-B. Chen, K.-S. Liu, M. Gao, and C.-F. Zhou, “Spatiotemporal evolution of land subsidence around a subway using InSAR time-series and the entropy method,” GIScience & Remote Sensing, vol. 54, no. 1, pp. 78–94, 2016.
View at: Publisher Site | Google Scholar
B. Bayer, A. Simoni, D. Schmidt, and L. Bertello, “Using advanced InSAR techniques to monitor landslide deformations induced by tunneling in the Northern Apennines, Italy,” Engineering Geology, vol. 226, pp. 20–32, 2017.
View at: Publisher Site | Google Scholar
K. Teshebaeva, H. Echtler, and B. Bookhagen, “Deep-seated gravitational slope deformation (DSGSD) and slow-moving landslides in the southern Tien Shan Mountains: new insights from InSAR, tectonic and geomorphic analysis,” Earth Surface Processes and Landforms, vol. 44, no. 12, pp. 2333–2348, 2019.
View at: Publisher Site | Google Scholar
J. Scoular, R. Ghail, P. Mason et al., “Retrospective InSAR analysis of east london during the construction of the lee tunnel,” Remote Sensing, vol. 12, p. 5, 2020.
View at: Publisher Site | Google Scholar
Y. Chen, K. Tan, S. Y. Yan et al., “Monitoring land surface displacement over Xuzhou (China) in 2015–2018 through PCA-based correction applied to SAR interferometry,” Remote Sensing, vol. 11, p. 12, 2019.
View at: Publisher Site | Google Scholar
C. Noviello, S. Verde, V. Zamparelli et al., “Buildings at landslide risk with SAR: a methodology based on the use of multipass interferometric data,” IEEE Geoscience and Remote Sensing Magazine, vol. 8, no. 1, pp. 91–119, 2019.
View at: Publisher Site | Google Scholar
J. Zhou, P. Ezquerro, J. A. Fernández-Merodo et al., “Analysis of an open-pit slope failure: las cruces case study, Spain,” Landslides, vol. 17, no. 9, pp. 2173–2188, 2020.
View at: Publisher Site | Google Scholar
Ł. Rudziński, K. Mirek, and J. Mirek, “Rapid ground deformation corresponding to a mining-induced seismic event followed by a massive collapse,” Natural Hazards, vol. 96, no. 1, pp. 461–471, 2018.
View at: Publisher Site | Google Scholar
A. J. García, M. Marchamalo, R. Martínez, B. González-Rodrigo, and C. González, “Integrating geotechnical and SAR data for the monitoring of underground works in the Madrid urban area: application of the persistent scatterer interferometry technique,” International Journal of Applied Earth Observation and Geoinformation, vol. 74, pp. 27–36, 2018.
View at: Publisher Site | Google Scholar
M. González, I. Armaș, P. Dumitru, and M. Necsoiu, “Subway construction using Sentinel-1 data: a case study in Bucharest, Romania,” International Journal of Remote Sensing, vol. 41, no. 7, pp. 2644–2663, 2019.
View at: Publisher Site | Google Scholar
R. Guo, S. M. Li, Y. N. Chen, X. Li, and L. Yuan, “Identification and monitoring landslides in longitudinal range-gorge region with InSAR fusion integrated visibility analysis,” Landslides, vol. 15, 2020.
View at: Publisher Site | Google Scholar
G. Giardina, P. Milillo, M. J. DeJong et al., “Evaluation of InSAR monitoring data for post-tunnelling settlement damage assessment,” Structural Control and Health Monitoring, vol. 26, no. 2, Article ID e2285, 2018.
View at: Publisher Site | Google Scholar
X. G. Shi, Q. Xu, and L. Zhang, “Surface displacements of the heifangtai terrace in Northwest China measured by X and C-band InSAR observations,” Engineering Geology, vol. 259, 2019.
View at: Publisher Site | Google Scholar
P. Milillo, G. Giardina, and M. DeJong, “Multi-temporal InSAR structural damage assessment: the london crossrail case study,” Remote Sensing, vol. 10, p. 2, 2018.
View at: Publisher Site | Google Scholar
X. Hu, R. Burgmann, W. H. Schulz, and F. J. Fielding, “Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing,” Nature Communications, vol. 11, no. 1, p. 2792, 2020.
View at: Publisher Site | Google Scholar
L. Zhou, J. Guo, J. Hu, J. Maau, F. Weiau, and X. Xue, “Analysis of ELH Bridge through ground-based interferometric radar during the crossing of a subway shield tunnel underneath the bridge,” International Journal of Remote Sensing, vol. 39, no. 6, pp. 1911–1928, 2017.
View at: Publisher Site | Google Scholar
M. Pauciullo, D. Piacentini, and E. Tirincanti, “Detection and monitoring of tunneling induced ground movements using sentinel-1 SAR interferometry,” Remote Sensing, vol. 11, p. 6, 2019.
View at: Publisher Site | Google Scholar
A. A. Malinowska, W. T. Witkowski, and R. Hejmanowski, “Sinkhole occurrence monitoring over shallow abandoned coal mines with satellite-based persistent scatterer interferometry,” Engineering Geology, vol. 262, 2019.
View at: Publisher Site | Google Scholar
G. Strecker, M. Komac, and M. Jemec, “PS-InSAR displacements related to soil creep and rainfall intensities in the Alpine foreland of western Slovenia,” Geomorphology, vol. 175, p. 176, 2012.
View at: Google Scholar
C. Crippa, F. Franzosi, M. Zonca et al., “Unraveling spatial and temporal heterogeneities of very slow rock-slope deformations with targeted DInSAR analyses,” Remote Sensing, vol. 12, p. 8, 2020.
View at: Publisher Site | Google Scholar
M. Khorrami, B. Alizadeh, and T. E. Ghasemi, “How groundwater level fluctuations and geotechnical properties lead to asymmetric subsidence: a PSInSAR analysis of land deformation over a transit corridor in the los angeles metropolitan area,” Remote Sensing, vol. 11, p. 4, 2019.
View at: Publisher Site | Google Scholar
B. W. Lowry, S. Baker, and W. Zhou, “A case study of novel landslide activity recognition using ALOS-1 InSAR within the ragged mountain western hillslope in gunnison county, Colorado, USA,” Remote Sensing, vol. 12, p. 12, 2020.
View at: Publisher Site | Google Scholar
X. T. Chen, X. F. He, and X. L. Yang, “Terrain point cloud assisted GB-InSAR slope and pavement deformation differentiate method in an open-pit mine,” Sensors (Basel)., vol. 20, p. 8, 2020.
View at: Publisher Site | Google Scholar
M. Bădescu, A. Gaber, F. Abdalla et al., “Use of optical and radar remote sensing satellites for identifying and monitoring active/inactive landforms in the driest desert in Saudi Arabia,” Geomorphology, vol. 362, 2020.
View at: Publisher Site | Google Scholar
D. Marsh, G. Wang, T. Yang, and F. Zhang, “Remote sensing-based detection of the deformation of a reservoir bank slope in Laxiwa hydropower station, China,” Landslides, vol. 10, no. 2, pp. 231–238, 2015.
View at: Publisher Site | Google Scholar
A. Hu, M. T. Hendry, and R. Macciotta, “GB-InSAR monitoring of vegetated and snow-covered slopes in remote mountainous environments,” Landslides, vol. 17, no. 7, pp. 1713–1726, 2017.
View at: Publisher Site | Google Scholar
Y. Tian, J. Liu-Zeng, Y. Luo et al., “Deformation during the Milashan Tunnel construction in northern sangri-cuona rift, Southern Tibet, China observed by Sentinel-1 satellites,” Science Bulletin, vol. 63, no. 21, pp. 1439–1447, 2018.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2020 Jiaxin Jia. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

547

Downloads

727

Citations