# Ground Deformation Revealed by Sentinel-1 MSBAS-InSAR Time-Series over Karamay Oilfield, China

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

## 3. Methods and Data

#### 3.1. Stacking Method

#### 3.2. Multidimensional Small Baseline Subset Method

#### 3.3. Data and Processing

## 4. Results

#### 4.1. Deformation Velocity

#### 4.2. Two-Dimensional Time Series Deformation

## 5. Analysis and Discussion

#### 5.1. Deformation Analysis

#### 5.2. Inversion Modeling

#### 5.2.1. Mogi Modeling

^{5}m

^{3}and 3.87 × 10

^{4}m

^{3}, respectively. Most of the residuals were smaller than 10 mm (Figure 11c,f). However, a relatively large residual occurred on the long axis of the elliptical deformation for B1. This observation indicates that the point source Mogi model is suitable for circular surface deformation, but when the deformation of the surface approximates an ellipse, it does not perform well anymore. For area C1, the deformation characteristics are close to circular, so the Mogi model performs adequately.

#### 5.2.2. Sill Modeling

^{5}and 2.15 × 10

^{5}m

^{3}, respectively) were very similar to each other. Similar to the C1 area, the volume changes (3.2 × 10

^{4}to 4.4 × 10

^{4}m

^{3}) from Mogi model were also close to the volume changes (2.2 × 10

^{4}to 3.5 × 10

^{4}m

^{3}) from Sill model. Unfortunately, we cannot compare our modeled volumes with the actual volume mined, because it is not available in the published data.

#### 5.3. Deformation and Seismicity

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Li, X.; Li, W.; Gao, B.; Yang, D. Study on subsurface water injection in Qizhong area of Karamay oilfield. Xinjiang Oil Gas.
**2012**, 3, 57–59. [Google Scholar] - Pan, S. Discussion on oil exploitation technology and oil field water Injection. Chem. Intermed.
**2018**, 7, 70–71. [Google Scholar] - Khakim, M.Y.N.; Tsuji, T.; Matsuoka, T. Geomechanical modeling for InSAR-derived surface deformation at steam-injection oil sand fields. J. Pet. Sci. Eng.
**2012**, 96, 152–161. [Google Scholar] [CrossRef] - Guéguen, Y.; Deffontaines, B.; Fruneau, B.; Al Heib, M.; de Michele, M.; Raucoules, D.; Planchenault, J.; Guise, Y. Monitoring residual mining subsidence of Nord/Pas-de-Calais coal basin from differential and Persistent Scatterer Interferometry (Northern France). J. Appl. Geophys.
**2009**, 69, 24–34. [Google Scholar] [CrossRef] - Martínez-Garzón, P.; Kwiatek, G.; Bohnhoff, M.; Dresen, G. Volumetric components in the earthquake source related to fluid injection and stress state. Geophys. Res. Lett.
**2017**, 44, 800–809. [Google Scholar] [CrossRef] [Green Version] - Ji, L.; Zhang, Y.; Wang, Q.; Xin, Y.; Li, J. Detecting land uplift associated with enhanced oil recovery using InSAR in the Karamay oil field, Xinjiang, China. Int. J. Remote Sens.
**2016**, 37, 1527–1540. [Google Scholar] [CrossRef] - Lu, Z.; Dzurisin, D. InSAR imaging of Aleutian volcanoes. In InSAR Imaging of Aleutian Volcanoes; Springer: Berlin/Heidelberg, Germany, 2014; pp. 87–345. [Google Scholar]
- Massonnet, D.; Briole, P.; Arnaud, A. Deflation of Mount Etna monitored by spaceborne radar interferometry. Nature
**1995**, 6532, 567–570. [Google Scholar] [CrossRef] - Lu, Z.; Mann, D.; Freymueller, J.T.; Meyer, D.J. Synthetic aperture radar interferometry of Okmok volcano, Alaska: Radar observations. J. Geophys. Res. Solid Earth
**2000**, B5, 10791–10806. [Google Scholar] [CrossRef] - Massonnet, D.; Rossi, M.; Carmona, C.; Adragna, F.; Peltzer, G.; Feigl, K.; Rabaute, T. The displacement field of the landers earthquake mapped by radar interferometry. Nature
**1933**, 64, 138–142. [Google Scholar] [CrossRef] - Zebker, H.A.; Rosen, P.A.; Goldstein, R.M.; Gabriel, A.; Werner, C.L. On the derivation of coseismic displacement fields using differential radar interferometry: The landers earthquake. J. Geophys. Res. Solid Earth
**1994**, 99, 19617–19634. [Google Scholar] [CrossRef] - Tantianuparp, P.; Shi, X.; Zhang, L.; Balz, T.; Liao, M. Characterization of landslide deformations in three Gorges area using Multiple InSAR Data Stacks. Remote Sens.
**2013**, 5, 2704–2719. [Google Scholar] [CrossRef] - Zhao, C.; Lu, Z.; Zhang, Q.; Fuente, J.D.L. Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA. Remote Sens. Environ.
**2012**, 124, 348–359. [Google Scholar] [CrossRef] - Goldstein, R.M.; Engelhardt, H.; Kamb, B.; Frolich, R.M. Satellite radar interferometry for monitoring ice sheet motion: Application to an Antarctic ice stream. Science
**1993**, 262, 1525–1530. [Google Scholar] [CrossRef] [PubMed] - Michel, R.; Rignot, E. Flow of Glaciar Moreno, Argentina, from repeat-pass Shuttle Imaging Radar images: Comparison of the phase correlation method with radar interferometry. J. Glaciol.
**1999**, 45, 93–100. [Google Scholar] [CrossRef] - Lubis, A.M.; Sato, T.; Tomiyama, N.; Isezaki, N.; Yamanokuchi, T. Ground subsidence in Semarang-Indonesia investigated by ALOS–PALSAR satellite SAR interferometry. J. Asian Earth Sci.
**2011**, 40, 1079–1088. [Google Scholar] [CrossRef] - Zhao, C.; Zhang, Q.; Ding, X.; Peng, J. Research on the characteristics of land subsidence and ground fissure development in Xi’an based on InSAR. J. Eng. Geol.
**2009**, 5, 1214–1222. [Google Scholar] - Milillo, P.; Giardina, G.; DeJong, M.J.; Perissin, D.; Milillo, G. Multi-Temporal InSAR structural damage assessment: The London crossrail case study. Remote Sens.
**2018**, 10, 287. [Google Scholar] [CrossRef] - Giardina, G.; Milillo, P.; DeJong, M.; Perissin, D.; Milillo, G. Evaluation of InSAR monitoring data for post-tunnelling settlement damage assessment. Struct. Control Health Monit.
**2019**, 26, e2285. [Google Scholar] [CrossRef] - Roccheggiani, M.; Piacentini, D.; Tirincanti, E.; Perissin, D.; Menichetti, M. Detection and monitoring of tunneling induced ground movements using Sentinel-1 SAR interferometry. Remote Sens.
**2019**, 11, 639. [Google Scholar] [CrossRef] - Juncu, D.; Árnadóttir, T.; Geirsson, H.; Guðmundsson, G.B.; Lund, B.; Gunnarsson, G.; Michalczewska, K. Injection-induced surface deformation and seismicity at the Hellisheidi geothermal field, Iceland. J. Volcanol. Geotherm. Res.
**2018**. [Google Scholar] [CrossRef] - Yang, Q.; Zhao, W.; Dixon, T.H.; Amelung, F.; Han, W.S.; Li, P. InSAR monitoring of ground deformation due to CO2 injection at an enhanced oil recovery site, West Texas. Int. J. Greenh. Gas Control
**2015**, 41, 20–28. [Google Scholar] [CrossRef] [Green Version] - Loesch, E.; Sagan, V. SBAS analysis of induced ground surface deformation from wastewater injection in east central Oklahoma, USA. Remote Sens.
**2018**, 10, 283–299. [Google Scholar] [CrossRef] - Zhu, Z.; Du, M.; Han, H. New technology for shallow super heavy oil exploitation in Karamay oilfield. J. Oil Gas Technol.
**2007**, 3, 441–443. [Google Scholar] - Pang, P. Karamay oilfield—the first large oilfield in New China. J. Univ. Pet. China
**2001**, 4, 29–32. [Google Scholar] - Aimaiti, Y.; Yamazaki, F.; Liu, W.; Kasimu, A. Monitoring of land-surface deformation in the Karamay oilfield, Xinjiang, China, Using SAR Interferometry. Appl. Sci.
**2017**, 7, 772–787. [Google Scholar] [CrossRef] - Teatini, P.; Gambolati, G.; Ferronato, M.; Settari, A.; Walters, D. Land uplift due to subsurface fluid injection. J. Geodyn.
**2011**, 51, 1–16. [Google Scholar] [CrossRef] [Green Version] - Samsonov, S.; d’Oreye, N. Multidimensional time-series analysis of ground deformation from multiple InSAR data sets applied to Virunga volcanic province. Geophys. J. Int.
**2012**, 191, 1095–1108. [Google Scholar] - Samsonov, S.V.; Feng, W.; Peltier, A. Multidimensional Small Baseline Subset (MSBAS) for volcano monitoring in two dimensions: Opportunities and challenges. Case study Piton de la Fournaise volcano. J. Volcanol. Geotherm. Res.
**2017**, 344, 121–138. [Google Scholar] [CrossRef] - Samsonov, S.V.; D’oreye, N. Multidimensional Small Baseline Subset (MSBAS) for two-dimensional deformation analysis: Case study Mexico City. Can. J. Remote Sens.
**2017**, 18, 318–329. [Google Scholar] [CrossRef] - Song, Z.Q.; Tan, C.Q.; Liu, S.S.; Wu, S.B.; Gao, X.J.; Qin, J.H.; Luo, Z.X. Study on reserve distribution and recovery percent of Heterogenerous conglomerate oil pools—an example front Upper Karamay formation reservoir in Karamay oilfield. Xinjiang Pet. Geol.
**2001**, 22, 335–337. [Google Scholar] - Zi-qi, S.; Hong, W.; Jun-feng, Y.; Wei, H.; Ting, P.; Kun-Peng, C. The research of reservoir tectonic feature of Karamay group in Qizhong and Qidong area in Karamay oilfield. West China Pet. Geosci.
**2006**, 4, 46–49. [Google Scholar] - Wang, Y. Glutenite Reservoir Heterogeneity Study of Upper Karamay Formation in Wu2dong Area of Karamay Oilfield; China University of Petroleum: Beijing, China, 2016. [Google Scholar]
- Yin, S.; Chen, G.; Chen, Y.; Wu, X. Control effect of pore structure modality on remaining oil in glutenite reservoir: A case from lower Karamay Formation in block Qidong 1 of Karamay oilfield. Lithhologic Reserv.
**2018**, 30, 91–102. [Google Scholar] - Sandwell, D.T.; Price, E.J. Phase gradient approach to stacking interferograms. J. Geophys. Res. Solid Earth
**1998**, 103, 30183–30204. [Google Scholar] [CrossRef] [Green Version] - Sandwell, D.T.; Sichoix, L. Topographic phase recovery from stacked ERS interferometry and a low-resolution digital elevation model. J. Geophys. Res. Solid Earth
**2000**, 105, 28211–28222. [Google Scholar] [CrossRef] - Ali, S.T.; Feigl, K.L. A new strategy for estimating geophysical parameters from InSAR data: Application to the Krafla central volcano in Iceland. Geochem. Geophys. Geosystems
**2012**, 13. [Google Scholar] [CrossRef] [Green Version] - Liu, C. Research on Time Series InSAR Technology for Structural Deformation Monitoring; Chang’an University: Xi’an, China, 2017. [Google Scholar]
- Wright, T.J. Toward mapping surface deformation in three dimensions using InSAR. Geophys. Res. Lett.
**2004**, 31, 169–178. [Google Scholar] [CrossRef] - Jin-Woo, K.; Zhong, L.; Kimberly, D. Ongoing deformation of sinkholes in Wink, Texas, observed by time-series Sentinel-1A SAR interferometry. Remote Sens.
**2016**, 4, 313–324. [Google Scholar] - Samsonov, S.; Czarnogorska, M.; White, D. Satellite interferometry for high-precision detection of ground deformation at a carbon dioxide storage site. Int. J. Greenh. Gas Control
**2015**, 42, 188–199. [Google Scholar] [CrossRef] - Hansen, P.C.; O’Leary, D.P. The use of the L-Curve in the regularization of discrete ill-posed problems. SIAM J. Sci. Comput.
**1993**, 6, 1487–1505. [Google Scholar] [CrossRef] - Ouyang, L.; Li, X.; Hui, F.; Zhang, B.; Cheng, X. Sentinel-1A data products’ characters and the potential applications. Chin. J. Polar Res.
**2017**, 29, 286–295. [Google Scholar] - Werner, C.; Wegmüller, U.; Strozzi, T.; Wiesmann, A. Gamma SAR and interferometric processing software. In Proceedings of the Ers-Envisat Symposium, Gothenburg, Sweden, 16–20 October 2000; pp. 1620–1629. [Google Scholar]
- Zhang, Y.; Wang, P.; Luo, X.; Zhang, Q.; Chen, H. Monitoring Xi’an land subsidence using Sentinel-1 images and SBAS-InSAR technology. Bull. Surv. Mapp.
**2017**, 29, 100–104. [Google Scholar] - Goldstein, R.M.; Werner, C.L. Radar interferogram filtering for geophysical applications. Geophys. Res. Lett.
**1998**, 25, 4035–4038. [Google Scholar] [CrossRef] [Green Version] - Costantini, M. A novel phase unwrapping method based on network programming. IEEE Trans. Geosci. Remote Sens.
**1998**, 3, 813–821. [Google Scholar] [CrossRef] - Dong, S.; Samsonov, S.; Yin, H.; Hang, L. Two-dimensional ground deformation monitoring in Shanghai based on SBAS and MSBAS InSAR methods. J. Earth Sci.
**2018**, 29, 960–968. [Google Scholar] [CrossRef] - Grzovic, M.; Ghulam, A. Evaluation of land subsidence from underground coal mining using time SAR (SBAS and PSI) in Springfield, Illinois, USA. Nat. Hazards
**2015**, 79, 1739–1751. [Google Scholar] [CrossRef] - Liu, G.; Tang, Y.; Wu, F.; Qin, X.; Zeng, A. Coordinates and velocities of crustal movement observation network in China. J. Geod. Geodyn.
**2012**, 32, 53–56. [Google Scholar] - Yi, C. Study on Distribution Regularities about the Remaining Oil of the Upper Karamay Reservoir in Wu2Dong Area of Karamay Oil Field; China University of Petroleum: Beijing, China, 2016. [Google Scholar]
- Liu, Y. Correlation Analysis between InSAR Technology Monitoring and Subsurface Fluid Mining in the Yellow River Delta; Chang’an University: Xi’an, China, 2016. [Google Scholar]
- Mogi, K. Relations between the eruptions of various volcanoes and the deformations of the ground surfaces around them. In Bulletin of the Earthquake Research Institute; University of Tokyo: Tokyo, Japan, 1958; Volume 36, pp. 99–134. [Google Scholar]
- Okada, Y. Surface deformation due to shear and tensile faults in a half space. Bull. Seismol. Soc. Am.
**1992**, 82, 1018–1040. [Google Scholar] - Zheng, Z.; Wang, W.; Chen, H.; Zhang, F. Alluvial Fan Conglomerate Reservoir Sedimentary Characteristics of Lower Karemary Formation in the 6th District of Karemary Oilfield. Sci. Technol. Rev.
**2009**, 27, 52–56. [Google Scholar] - Suckale, J. Moderate-to-large seismicity induced by hydrocarbon production. Lead. Edge
**2010**, 29, 310–319. [Google Scholar] [CrossRef]

**Figure 1.**Study area and the SAR image coverage. Top left inset displays the location of our study area in China. Blue solid line and red solid line indicate the coverage of the ascending (Path 114) and descending images (Path 165) from Sentinel-1, respectively. Black solid marked the specific region of interest and the black lines are faults. Earthquakes (1.0 ≤ Mw) occurring between March 2017 and August 2018 are shown with color circles. Size and color of circles represent earthquake magnitude and depth, respectively. Purple dashed curve shows the locations of reservoirs. Green circles show two administrative districts (Karamay and Baijiantan). Black circles show the Jinlong town and a railway station located near the oilfield.

**Figure 2.**Simplified schematics of two-dimensional time series. Ascending and descending SAR acquisitions at time t

_{−1}to t

_{6}are marked with small black circles and blue circles, respectively. The horizontal solid lines between two dots represent interferograms, which are marked with I

_{1}to I

_{6}. ∆t

_{1}to ∆t

_{5}represent the time intervals between acquired adjacent images (modified from Samsonov [29]).

**Figure 3.**Networks of InSAR pairs. (

**a**) Ascending (Path 114), and (

**b**) descending (Path 165). Blue diamond symbols and black lines represent images and interferograms, respectively.

**Figure 4.**Stacking-derived deformation velocity of the Karamay oilfield from March, 2017 to August, 2018. Deformation rate map derived from (

**a**) ascending (Path 114), and (

**b**) descending (Path 165) tracks. The black triangle on the left of area B represents the location of GPS station XJKL. White arrows represent the orbit direction (ascending and descending) and the red arrows indicate the look vectors of images.

**Figure 5.**Vertical and horizontal deformation rate maps. (

**a**) Positive and negative values represent uplift and subsidence, respectively. (

**b**) Positive and negative values represent eastward and westward movement, respectively. P1–P6 are the points at which the time-series deformation in horizontal and vertical planes is plotted in Figure 9. The triangle denotes the location of a GPS station, XJKL, and rectangles outlined with a dashed line mark four areas, A to D. The smaller rectangles outlined with a solid line mark sub-areas B1 and C1. The upper left insert is an expanded view of the black rectangular frame in region B and the line i–j are the time series profiles of cumulative deformation.

**Figure 6.**Time series of vertical deformation in area B, estimated by MSBAS in the period 31 March 2017 to 15 August 2018.

**Figure 7.**Time series of horizontal deformation in area B, estimated by MSBAS in the period 31 March 2017 to 15 August 2018.

**Figure 8.**(

**a**) Vertical and (

**b**) horizontal deformation time series from InSAR and GPS at station XJKL.

**Figure 9.**Vertical and east–west deformation time series selected points (

**a**–

**f**) at points P1–P6, respectively. Triangles and diamond symbols represent the vertical (U–D) and horizontal (E–W) time series deformations, respectively. The red error bars indicate the standard deviation of the average of 5 × 5 pixels in the vertical deformation and the blue error bars show the errors for the horizontal deformation.

**Figure 10.**Deformation time series along line i–j in Figure 5, in the (

**a**) vertical and (

**b**) east–west directions.

**Figure 11.**Results from the Mogi model for areas B1 and C1. (

**a**,

**d**) Cumulative deformation map for B1 and C1 (Figure 5a); (

**b**,

**e**) best-fitting model result from the Mogi model for B1 and C1; and (

**c**,

**f**) maps of residuals between observed and modeled values for B1 and C1.

**Figure 12.**Result from the Sill model for B1 and C1. (

**a**,

**d**) Cumulative deformation map for B1 and C1 (Figure 5a); (

**b**,

**e**) best-fitting model result from the Sill model for B1 and C1; and (

**c**,

**f**) maps of residuals between observed and modeled values for B1 and C1.

**Figure 13.**Mohr–Coulomb circles. τ

_{1}an τ

_{2}are the current and maximum allowable shear stress, respectively. ${\mathsf{\sigma}}_{1}$ and ${\mathsf{\sigma}}_{2}$ are the maximum and minimum principal stress, respectively. The Mohr–Coulomb circles move to the left when pore pressure (p

_{0}) increases (modified from Teatini [27]).

**Figure 14.**Earthquake hypocenters within 70 km of the Karamay oilfield region between 2006 and 2008. Circle size represents earthquake magnitude, black and blue dashed lines mark the upper (300 m) and lower (3000 m) boundaries of the reservoir, and red dotted line marks the depth obtained from the inversion (600 m).

Parameter | Optimization (B1) | Optimization (C1) |
---|---|---|

X (km) | 1.61 ± 0.02 | 0.57 ± 0.01 |

Y (km) | 1.60 ± 0.03 | 0.67 ± 0.02 |

Depth (m) | 580 ± 30 | 370 ± 20 |

Volume change ($\Delta V$, m^{3}) | 1.97$\text{}\times \text{}{10}^{5}$ ± 1.1 $\times $ ${10}^{4}$ | 3.87$\times \text{}{10}^{4}$ ± 5.9 $\times \text{}{10}^{3}$ |

Parameter | Optimization (B1) | Optimization (C1) |
---|---|---|

X (km) | 1.68 ± 0.02 | 0.61 ± 0.01 |

Y (km) | 1.63 ± 0.02 | 0.67 ± 0.01 |

Depth (m) | 606 ± 30 | 422 ± 30 |

Strike (°) | 270° ± 3° | 189° ± 2° |

Length (km) | 1.00 ± 0.07 | 0.29 ± 0.03 |

Width (km) | 0.23 ± 0.03 | 0.26 ± 0.01 |

Opening (m) | 1.26 ± 0.2 | 0.39 ± 0.05 |

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## Share and Cite

**MDPI and ACS Style**

Yang, C.; Zhang, D.; Zhao, C.; Han, B.; Sun, R.; Du, J.; Chen, L.
Ground Deformation Revealed by Sentinel-1 MSBAS-InSAR Time-Series over Karamay Oilfield, China. *Remote Sens.* **2019**, *11*, 2027.
https://doi.org/10.3390/rs11172027

**AMA Style**

Yang C, Zhang D, Zhao C, Han B, Sun R, Du J, Chen L.
Ground Deformation Revealed by Sentinel-1 MSBAS-InSAR Time-Series over Karamay Oilfield, China. *Remote Sensing*. 2019; 11(17):2027.
https://doi.org/10.3390/rs11172027

**Chicago/Turabian Style**

Yang, Chengsheng, Dongxiao Zhang, Chaoying Zhao, Bingquan Han, Ruiqi Sun, Jiantao Du, and Liquan Chen.
2019. "Ground Deformation Revealed by Sentinel-1 MSBAS-InSAR Time-Series over Karamay Oilfield, China" *Remote Sensing* 11, no. 17: 2027.
https://doi.org/10.3390/rs11172027