GNSS Ground deformation observation network optimization assisted using prior InSAR-derived ground surface deformation and multiscale iteration estimation

ABSTRACT

Ground surface deformation must be monitored to understand and prevent geological hazards. Global navigation satellite system (GNSS) technology has been widely used in deformation monitoring; however, current GNSS station layout strategies are often subjective and lack theoretical guidance. Therefore, in this study, a GNSS deformation observation network optimization method was developed based on interferometric synthetic aperture radar (InSAR) prior to deformation assistance. This method considers the deformation distribution, cost, accuracy, topography, and actual monitoring requirements and uses kriging interpolation and multiscale iterative optimization to obtain the reference number, spatial distribution, and deformation capture accuracy of GNSS stations in areas experiencing deformation. Simulation experiments showed that the proposed method strengthened the spatial deformation monitoring ability of GNSS by 46%–68% compared with the traditional station layout methods, which ignore the distribution of the deformation. Then, based on the small baseline subset (SBAS) InSAR technology and applying the new method, we evaluated the number and location of the most suitable GNSS stations for a mining area and found that 89.6% of the error was less than 1.5 cm, which further illustrates the practicability and reliability of the proposed method. This method provides an effective supplement to station layouts for monitoring GNSS deformation.

KEYWORDS:

• Ground deformation monitoring
• GNSS station layout
• InSAR
• multiscale optimization

Acknowledgements

The authors wish to thank the Japan Aerospace Exploration Agency (JAXA) for providing the ALOS-2/PALSAR-2 data.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The ALOS-2/PALSAR-2 data used in this paper can be applied for on the https://auig2.jaxa.jp/ips/home. Other data in this paper can be obtained by contacting the authors.

Additional information

Funding

This work was partly supported by the National Natural Science Fund for Distinguished Young Scholars, grant number 41925016; the National Natural Science Foundation of China, grant number 42330112; and the Innovation Foundation for Postgraduate of Central South University under grant 2021zzts0264.