Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

Yan, Hongge; Dai, Wujiao; Liu, Hongzhi; Gao, Han; Neely, W.; Xu, Wenbin

doi:10.3390/rs14143293

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…BDS monitoring station can measure both horizontal and vertical deformations. The displacement of the BDS base station can be converted into the Line-of-Sight (LOS) direction of InSAR 23 . This conversion is represented by Eq.…”

Section: Methodsmentioning

confidence: 99%

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

Dongwei,

Yuci,

Yuzheng

et al. 2024

Sci Rep

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Environmental changes and ground subsidence along railway lines are serious concerns during high-speed railway operations. It is worth noting that AutoRegressive Integrated Moving Average (ARMA), Long Short-Term Memory (LSTM), and other prediction methods may present limitations when applied to predict InSAR time series results. To address this issue, this study proposes a prediction method that decomposes the nonlinear settlement time series of feature points obtained through InSAR technology using Ensemble Empirical Mode Decomposition (EEMD). Subsequently, multiple Intrinsic Mode Functions (IMFs) are generated, and each IMF is individually predicted using the Prophet forecasting model. Finally, we employ an equal-weight superimposition method to combine the results, resulting in the prediction of the InSAR settlement time series. The predicted values of each component are subsequently weighted equally and combined to derive the final prediction outcome. This paper selects InSAR monitoring data along a high-speed railway in inland China and uses the proposed method and ARMA and Prophet models to carry out comparative experiments. The experimental results show that compared with the ARMA and Prophet models, the method in this paper improves the root mean square error by 58.01% and 32.3%, and increases the mean absolute error by 62.69% and 33.78%, respectively. The predicted settlement values generated by our method exhibit better agreement with the actual InSAR monitoring values.

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Section: Methodsmentioning

confidence: 99%

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

Dongwei,

Yuci,

Yuzheng

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…(2) medium-wavelength errors and topography-related errors are often exist in interferograms and cannot be eliminated (Li et al 2019;Morishita and Kobayashi 2022;Yan et al 2022a). In addition, the deformation information in interferograms is relative to the reference point.…”

Section: Insar Interferogram Data Processingmentioning

confidence: 99%

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Yan,

Dai,

et al. 2024

Earth Planets Space

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Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios. Graphical Abstract

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LSC-GInSAR: a GNSS-enhanced InSAR approach by using least squares collocation

Chen,

Shen

2023

Geophysical Journal International

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Summary High quality Interferometric Synthetic Aperture Radar (InSAR) interferograms are essential for determining surface deformation from InSAR time series. However, InSAR interferograms are usually polluted by spatially correlated errors (SCEs), especially the unmodelled atmospheric phase delays. To mitigate spatially correlated errors and improve the quality of InSAR interferograms, we propose a new approach to incorporate the Global Navigation Satellite System (GNSS) data from continuously operating reference stations for enhancing InSAR interferograms via modelling SCEs as signals and solving the signals together with the systematic parameters using Least Squares Collocation (LSC), which is referred to as the LSC-GInSAR approach. Our improvement for the GInSAR method of Neely et al. (2020) can correct more SCEs. The Sentinel-1 data over the southern Central Valley of California, USA, are processed with our LSC-GInSAR approach, which is compared to the GInSAR approach. The performance of the LSC-GInSAR approach is evaluated by external GNSS displacements. The results show that the LSC-GInSAR approach can effectively mitigate medium-to-long-wavelength SCEs. The displacements resolved by LSC-GInSAR are more consistent with the cGNSS observations than those resolved by GInSAR, with an average root mean square (RMS) improvement of 14.3 per cent. In addition, the LSC-GInSAR approach reduced the average standard deviations (STDs) of all 276 InSAR interferograms from 14.2 to 11.0 mm compared to that of the GInSAR approach.

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Fusion of Spatially Heterogeneous GNSS and InSAR Deformation Data Using a Multiresolution Segmentation Algorithm and Its Application in the Inversion of Slip Distribution

Cited by 6 publications

References 33 publications

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

Land subsidence analysis along high-speed railway based on EEMD-Prophet method

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

LSC-GInSAR: a GNSS-enhanced InSAR approach by using least squares collocation

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