High-resolution surface velocities and strain for Anatolia from Sentinel-1 InSAR and GNSS data

Weiss, Jonathan

doi:10.31223/osf.io/8xa7j

Cited by 20 publications

(49 citation statements)

References 45 publications

(53 reference statements)

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“…The next step is therefore to adapt our technique to more continuous pictures of the surface deformation as produced by optical image correlation (e.g., Barnhart et al., 2020; Delorme et al., 2020; Vallage et al., 2015) or InSAR (LOS velocities, e.g., Hussain et al., 2016; H. Wang et al., 2019; Weiss et al., 2020). One difficulty to do so is to properly account for the spatially correlated noise between pixels, fully described by a covariance matrix for InSAR (e.g., Lohman & Simons, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…Note that most GNSS studies only consider the horizontal 2D tensor

\dot{ϵ}

(D'Agostino, 2014; Ward, 1998) or a partially 3D tensor (Mazzotti et al., 2011; Shen et al., 2015) for two main reasons: (a) the vertical component of the GNSS velocity is often associated with large uncertainties (Bennett & Hreinsdóttir, 2007), and (b) we have no access to the vertical derivative of the velocity components ( ∂ z V x , ∂ z V y , ∂ z V z ). Joint GNSS‐InSAR studies also remain limited to a 2D strain tensor analysis (e.g., Weiss et al., 2020). In this study, we only consider the horizontal velocity field and corresponding 2D strain rate tensor, while discussing in Section 6 the possibility to include V z in future analysis.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Estimation of Surface Strain Rates From Global Navigation Satellite System Measurements: Application to the Southwestern United States

et al. 2021

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Imaging and quantifying the present-day lithospheric deformation is crucial to understanding how and where long-term tectonic loading is accommodated. Plate tectonics theory assumes that the relative motion of rigid lithospheric blocks is accommodated on a limited set of localized fault zones, where the lithosphere either deforms elastically during the interseismic period of the seismic cycle, or in a brittle way during the coseismic rupture (Isacks et al., 1968;Le Pichon, 1968;Morgan, 1968). In a simple elastic framework, the surface deformation generated by slip on a dislocation buried in an elastic half-space can be computed (e.g., Okada, 1985), as well as the surface deformation produced by full or partial locking of the buried fault using the "backslip" hypothesis (Savage, 1983). Analyzing the spatial patterns of surface deformation and their temporal variations around active faults can therefore help constraining the behavior of fault systems at each stage of the seismic cycle.

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Section: Discussionmentioning

confidence: 99%

“…Note that most GNSS studies only consider the horizontal 2D tensor

\dot{ϵ}

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Surface Strain Rates From Global Navigation Satellite System Measurements: Application to the Southwestern United States

et al. 2021

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“…Turkey is located in an arid environment and the region is not heavily vegetated, meaning the coherence is generally excellent and InSAR has been used for many tectonic studies (e.g. Cakir et al 2005;Burgmann et al 2002;Wright et al 2001;Weiss 2020). However, the high peaks volcanic peaks are covered with snow during the winter months, reducing coherence.…”

Section: Coherencementioning

confidence: 99%

Baseline monitoring of volcanic regions with little recent activity: application of Sentinel-1 InSAR to Turkish volcanoes

et al. 2021

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Volcanoes have dormancy periods that may last decades to centuries meaning that eruptions at volcanoes with no historical records of eruptions are common. Baseline monitoring to detect the early stages of reawakening is therefore important even in regions with little recent volcanic activity. Satellite techniques, such as InSAR, are ideally suited for routinely surveying large and inaccessible regions, but the large datasets typically require expert interpretation. Here we focus on Turkey where there are 10 Holocene volcanic systems, but no eruptions since 1855 and consequently little ground-based monitoring. We analyse data from the first five years of the European Space Agency Sentinel-1 mission which collects data over Turkey every 6 days on both ascending and descending passes. The high relief edifices of Turkey’s volcanoes cause two challenges: 1) snow cover during the winter months causes a loss of coherence and 2) topographically-correlated atmospheric artefacts could be misinterpreted as deformation. We propose mitigation strategies for both. The raw time series at Hasan Dag volcano shows uplift of ~ 10 cm between September 2017 and July 2018, but atmospheric corrections based on global weather models demonstrate that this is an artefact and reduce the scatter in the data to < 1 cm. We develop two image classification schemes for dealing with the large datasets: one is an easy to follow flowchart designed for non-specialist monitoring staff, and the other is an automated flagging system using a deep learning approach. We apply the deep learning scheme to a dataset of ~ 5000 images over the 10 Turkish volcanoes and find 4 possible signals, all of which are false positives. We conclude that there has been no cm-scale volcano deformation in Turkey in 2015–2020, but further analysis would be required to rule out slower rates of deformation (< 1 cm/yr). This study has demonstrated that InSAR techniques can be used for baseline monitoring in regions with few historical eruptions or little reported deformation.

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“…For example, the LOS velocity for the frame shown in Fig. 11a has been used to derive strain rates for Anatolia [64]. Figure 11.…”

Section: Value-added Licsar-based Products For Tectonic Studiesmentioning

confidence: 99%

LiCSAR: An Automatic InSAR Tool for Measuring and Monitoring Tectonic and Volcanic Activity

Lázecký¹

2020

Preprint

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Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large-scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present LiCSAR, an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 system). The products are continuously updated in a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products.

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High-resolution surface velocities and strain for Anatolia from Sentinel-1 InSAR and GNSS data

Cited by 20 publications

References 45 publications

Bayesian Estimation of Surface Strain Rates From Global Navigation Satellite System Measurements: Application to the Southwestern United States

Bayesian Estimation of Surface Strain Rates From Global Navigation Satellite System Measurements: Application to the Southwestern United States

Baseline monitoring of volcanic regions with little recent activity: application of Sentinel-1 InSAR to Turkish volcanoes

LiCSAR: An Automatic InSAR Tool for Measuring and Monitoring Tectonic and Volcanic Activity

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