A Kalman Filter Time Series Analysis Method for InSAR

Dalaison, Manon; Jolivet, Romain

doi:10.1029/2019jb019150

Cited by 31 publications

(29 citation statements)

References 64 publications

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“…Moreover, the proposed method can be further improved by adjusting the original deformation model functions (i.e., Equation (7)) based on a priori information about the deformation evolution process. For example, a time-related jump function (e.g., the Heaviside function [47]) can be used if there is an abrupt event.…”

Section: Discussionmentioning

confidence: 99%

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Liu

et al. 2021

Remote Sensing

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The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

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Section: Discussionmentioning

confidence: 99%

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Liu

et al. 2021

Remote Sensing

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“…The parametric model describes the evolution of deformation with a linear combination of predefined functions of time from which we optimize the L coefficients a i (0

\le i<

L). A well designed model conditions the accuracy of the estimated mean ground velocity (Dalaison & Jolivet, 2020). Notably, we must account for instantaneous phase changes caused by earthquakes.…”

Section: Methodsmentioning

confidence: 99%

The Interplay Between Seismic and Aseismic Slip Along the Chaman Fault Illuminated by InSAR

et al. 2021

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The 700‐km‐long Chaman fault (CF) marks the western edge of the plate boundary between India and Eurasia. Although global plate models predict 2.3–3.6 cm/yr left‐lateral motion between both plates, the fault is known to have hosted few earthquakes in historical times. Recent geodetic measurements attested the presence of aseismic slip locally. To detail the interplay between fast and slow slip along the CF, we build three Interferometric Synthetic‐Aperture Radar time series of ground deformation covering the whole fault length over 5 years (2014–2019). We find that most of the active fault trace slips aseismically and continuously. From south to north, we identify three creeping fault portions: the Nushki, Central, and Qalat segments of lengths between 80 and 130 km. The loading rate is 1.2 ± 0.3 cm/yr for the two southernmost portions, while it is about 0.7 ± 0.2 cm/yr for the Qalat segment. The Central segment and the nearby locked segments have hosted the largest known historical earthquakes on the CF, and three moderate magnitude earthquakes in our observation period. We image these earthquakes for which modeled slip at depth (Mw 5–5.6), time series of surface slip and deformation patterns argue toward large triggered aseismic slip. The June 2018 event displays postseismic moment 3–15 times greater than coseismic moment. Over the two decades covered by geodetic observations, continuous or triggered aseismic slip dominates along most of the fault and co‐locates with earthquakes. We observe that fault geometrical complexities delimit active segments and may be responsible for the kilometer‐scale intertwining between seismic and aseismic events.

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“…Therefore, significant effort has been put into developing strategies to build time series with such vast data sets, e.g., refs. 13 , 30 , 62 . Nonetheless automatic, autonomous InSAR interpretation methods are poised to become essential, if just to leverage the increasing spatial and temporal resolution of the data.…”

Section: Discussionmentioning

confidence: 99%

“…In the following, we consider the evolution of the interferometric phase with time with respect to a reference both in space and time. We consider classical Small Baseline (SBAS)-like approaches for the reconstruction of the time series 29 , 30 . The time series we analyze stem from the inversion of a sequence of SAR interferograms previously corrected from orbital and topographic contributions 31 , with a first-order atmospheric correction derived from global atmospheric reanalysis products 21 , 32 .…”

Section: Introductionmentioning

confidence: 99%

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

et al. 2021

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Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault’s location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

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A Kalman Filter Time Series Analysis Method for InSAR

Cited by 31 publications

References 64 publications

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

The Interplay Between Seismic and Aseismic Slip Along the Chaman Fault Illuminated by InSAR

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

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