Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations

Yu, Chen; Li, Zhenhong; Penna, Nigel T.; Crippa, Paola

doi:10.1029/2017jb015305

Cited by 447 publications

(283 citation statements)

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“…We detrended the interferograms by fitting a quadratic surface in the nondeforming area to account for the orbital (clearly seen in Figures S1 and S2) and possible ionospheric errors. Our experiments show that the root‐mean‐squares of the GACOS‐based (Yu et al, ; Yu, Li, & Penna, ; Yu, Li, Penna, & Crippa, ) corrected interferograms in the nondeforming areas are 6.9 and 4.3 cm for the ascending and descending interferograms, respectively, while those of the interferograms, corrected by the empirical topography‐correlated atmospheric phase, are lower (6.1 cm and 3.2 cm). Therefore, the empirical‐based corrected interferograms were finally used in this study (Figure ).…”

Section: Data and Modeling Methodsmentioning

confidence: 99%

The 2018 M_w 7.5 Papua New Guinea Earthquake: A Possible Complex Multiple Faults Failure Event With Deep‐Seated Reverse Faulting

Wang

et al. 2020

Earth and Space Science

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Although the Papuan fold and thrust belt (PFTB) is thought to accommodate convergence between the Pacific and Australian plates, the possible strain partitioning and its kinematic controls in the region are poorly understood. On 25 February 2018, a devastating Mw 7.5 earthquake jolted the southern area of the PFTB, central Papua New Guinea. The detailed imaging of the source parameters of this large earthquake is an urgent demand for a better understanding of the tectonism in the region. Here we used spaceborne interferometric synthetic aperture radar data to carefully determine the possible ruptured faults and associated fault geometries and further estimated the detailed slip of the 2018 Mw 7.5 earthquake. We find that the earthquake involved multiple faults failure, and a four‐segment fault model can reasonably produce the complex coseismic deformation, especially the observed bimodal displacements. The earthquake was dominated by reverse fault motion, and significant slip of up to ~0.7 m was documented on faults extending at depths great than 25 km. An integrated analysis of the fracture complexities suggests that possible strain partitioning in the PFTB could be tightly coupled with regional fault geometries and lithospheric properties. Our findings on the deep brittle failures suggest that crustal shortening across the PFTB could primarily control the growth of fold and thrust belt in the central Papua New Guinea.

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Section: Data and Modeling Methodsmentioning

confidence: 99%

The 2018 M_w 7.5 Papua New Guinea Earthquake: A Possible Complex Multiple Faults Failure Event With Deep‐Seated Reverse Faulting

Wang

et al. 2020

Earth and Space Science

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“…We use the Generic Atmospheric Correction Online Service (GACOS) to model stratified atmosphere, S , based on weather model data [30,18,31]. Zenith total delay (ZTD) maps are derived from the high-resolution water vapour delays (0.125 • and 6hour resolutions) generated by the European Centre for Medium-Range Weather Forecasts (ECMWF).…”

Section: Stratified Atmospherementioning

confidence: 99%

A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets

Anantrasirichai

Biggs

Albino

et al. 2019

Remote Sensing of Environment

129

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Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Synthetic Aperture Radar (InSAR), which can be employed to detect surface deformation with a strong statistical link to eruption. Recent developments in technology as well as improved computational power have resulted in unprecedented quantities of monitoring data, which can no longer be inspected manually. The ability of machine learning to automatically identify signals of interest in these large InSAR datasets has already been demonstrated, but data-driven techniques, such as convolutional neutral networks (CNN) require balanced training datasets of positive and negative signals to effectively differentiate between real deformation and noise. As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging than many other applications. In this paper, we address this problem using synthetic interferograms to train the AlexNet CNN. The synthetic interferograms are composed of 3 parts: 1) deformation patterns based on a Monte Carlo selection of parameters for analytic forward models, 2) stratified atmospheric effects derived from weather models and 3) turbulent atmospheric effects based on statistical simulations of correlated noise. The AlexNet architecture trained with synthetic data outperforms that trained using real interferograms alone, based on classification accuracy and positive predictive value (PPV). However, the models used to generate the synthetic signals are a simplification of the natural processes, so we retrain the CNN with a combined dataset consisting of synthetic models and selected real examples, achieving a final PPV of 82%. Although applying atmospheric corrections to the entire dataset is computationally expensive, it is relatively simple to apply them to the small subset of positive results. This further improves the detection performance without a significant increase in computational burden (PPV of 100%). Thus, we demonstrate that training with synthetic examples can improve the ability of CNNs to detect volcano deformation in satellite images, and propose an efficient workflow for the development of automated systems.

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“…(f) Comparison between ground velocity and topography that shows a strong correlation between InSAR and GACOS. them using the 90-m Shuttle Radar Topography Mission Digital Elevation Model (Yu et al, 2017;Yu, Li, Penna, & Crippa, 2018). We then convert the phase delays of GACOS model from zenith to the LOS of the interferograms.…”

Section: Methodsmentioning

confidence: 99%

Reevaluating Volcanic Deformation Using Atmospheric Corrections: Implications for the Magmatic System of Agung Volcano, Indonesia

Yip

Biggs

Albino

2019

Geophysical Research Letters

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A major challenge in using satellite interferometry (interferometric synthetic aperture radar) for volcanic monitoring in the tropics is distinguishing volcano deformation from atmospheric noise. We reanalyzed interferometric synthetic aperture radar time series from 2007-2009 from Agung volcano, Indonesia, which had previously been used as evidence for unrest. Using uncorrected data, we find an apparent velocity of 5.0 ± 2.7 cm/yr consistent with previous reports, but we show that this signal is consistent with predictions of atmospheric contributions derived from weather models (European Centre for Medium-Range Weather Forecasts-High-Resolution). Following the correction, we find a velocity of 1.4 ± 4.2 cm/yr and conclude that there was no significant deformation related to the inflation of a shallow magma source from 2007-2009. We discuss the implications for the inferred magma storage system at Agung and consider which other reported signals might have been wrongly attributed to deformation and should be reanalyzed. Plain Language SummarySpace-based volcano monitoring is difficult in the tropics for a number of reasons including cloud cover in optical images and decorrelation between radar images. A major challenge in satellite-based volcano monitoring in the tropics is distinguishing volcano deformation from atmospheric noise. We reanalyzed spaceborne radar data over Agung volcano, Indonesia, where deformation was reported between 2007 and 2009. Using a high-resolution weather model, we show that the apparent velocity signals of about 5 cm/yr observed at Agung were caused by atmospheric noise. We conclude that there are no significant deformation signals related to a shallow magma source from 2007-2009. Finally, we consider the implications for the magma plumbing system beneath Agung.

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Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations

Cited by 447 publications

References 59 publications

The 2018 M_w 7.5 Papua New Guinea Earthquake: A Possible Complex Multiple Faults Failure Event With Deep‐Seated Reverse Faulting

The 2018 M_w 7.5 Papua New Guinea Earthquake: A Possible Complex Multiple Faults Failure Event With Deep‐Seated Reverse Faulting

A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets

Reevaluating Volcanic Deformation Using Atmospheric Corrections: Implications for the Magmatic System of Agung Volcano, Indonesia

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Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations

Cited by 447 publications

References 59 publications

The 2018 Mw 7.5 Papua New Guinea Earthquake: A Possible Complex Multiple Faults Failure Event With Deep‐Seated Reverse Faulting

The 2018 Mw 7.5 Papua New Guinea Earthquake: A Possible Complex Multiple Faults Failure Event With Deep‐Seated Reverse Faulting

A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets

Reevaluating Volcanic Deformation Using Atmospheric Corrections: Implications for the Magmatic System of Agung Volcano, Indonesia

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The 2018 M_w 7.5 Papua New Guinea Earthquake: A Possible Complex Multiple Faults Failure Event With Deep‐Seated Reverse Faulting

The 2018 M_w 7.5 Papua New Guinea Earthquake: A Possible Complex Multiple Faults Failure Event With Deep‐Seated Reverse Faulting