Abstract:<p>Phase unwrapping is the process of recovering the absolute phase from unambiguous wrapped phase values that are measured modulo 2pi rad. From a mathematical point of view, phase unwrapping is an inverse problem, however, it is ill-posed and notoriously difficult to solve in the presence of noise. Meanwhile, phase unwrapping errors severely impact the estimation of earthquake and volcano source parameters using interferometric observations, therefore avoiding phase unwrapping completely is desi… Show more
“…In order to give equal weights to each data set during an inversion, we investigated several methods: one involves adjusting the number of subsampled points for each data set; the second, hereafter referred to as the Covariance Weighting inversion method, involves weighting the data through the covariance matrix; and the last, hereafter referred to as the Wrapped Inversion method, involves removing the influence of data magnitude by carrying out inversions on wrapped interferograms (Feigl & Thurber 2009;Jiang & González 2020).…”
Section: Methods For Compensating For Unbalanced Weights Of Insar Datamentioning
confidence: 99%
“…Another approach consists of carrying out inversions on wrapped data. This method has been proposed by Feigl & Thurber (2009); Ali & Feigl (2012) and improved by Jiang & González (2020) in order to overcome errors related to the phase-unwrapping step. Here, we use it in order to cancel the influence of data magnitudes.…”
Summary
InSAR data acquired from ascending and descending orbits are often characterized by different magnitudes of the observed Line-Of-Sight displacements, which may potentially bias inverse models. Using synthetic numerical models of dyke intrusions, we show that biased solutions are obtained when carrying out “conventional” inversions where only observation and modeling errors are taken into consideration. To mitigate the impact of the relative magnitudes of the data, we propose two methods: a covariance weighting inversion and a wrapped data inversion. These methods are compared to a conventional inversion using synthetic data generated by models of dykes of known geometry. We find that the covariance weighting method allows to retrieve an initial source geometry better than the other methods. These methods are then applied to the July 2017 eruption of Piton de la Fournaise. Using a covariance weighting inversion, the difference in fit between datasets decreases from 50 per cent to 20 per cent and the newly estimated source is in better agreement with the geological context.
“…In order to give equal weights to each data set during an inversion, we investigated several methods: one involves adjusting the number of subsampled points for each data set; the second, hereafter referred to as the Covariance Weighting inversion method, involves weighting the data through the covariance matrix; and the last, hereafter referred to as the Wrapped Inversion method, involves removing the influence of data magnitude by carrying out inversions on wrapped interferograms (Feigl & Thurber 2009;Jiang & González 2020).…”
Section: Methods For Compensating For Unbalanced Weights Of Insar Datamentioning
confidence: 99%
“…Another approach consists of carrying out inversions on wrapped data. This method has been proposed by Feigl & Thurber (2009); Ali & Feigl (2012) and improved by Jiang & González (2020) in order to overcome errors related to the phase-unwrapping step. Here, we use it in order to cancel the influence of data magnitudes.…”
Summary
InSAR data acquired from ascending and descending orbits are often characterized by different magnitudes of the observed Line-Of-Sight displacements, which may potentially bias inverse models. Using synthetic numerical models of dyke intrusions, we show that biased solutions are obtained when carrying out “conventional” inversions where only observation and modeling errors are taken into consideration. To mitigate the impact of the relative magnitudes of the data, we propose two methods: a covariance weighting inversion and a wrapped data inversion. These methods are compared to a conventional inversion using synthetic data generated by models of dykes of known geometry. We find that the covariance weighting method allows to retrieve an initial source geometry better than the other methods. These methods are then applied to the July 2017 eruption of Piton de la Fournaise. Using a covariance weighting inversion, the difference in fit between datasets decreases from 50 per cent to 20 per cent and the newly estimated source is in better agreement with the geological context.
“…Then, the fault slip distribution is forward modeled to estimate surface displacement. Following Jiang and González (2020), a misfit function is constructed based on the wrapped phase residuals and the weighting matrix. The misfit function is then regarded as the likelihood function fed into the Bayesian process to retrieve the posterior distribution of crack model parameters.…”
Section: Time‐dependent Fault Slip Inferred Using Geodetic Fault Slip...mentioning
confidence: 99%
“…To develop the kinematic fault model, we first constructed the fault geometry derived from a nonlinear fault inversion of InSAR wrapped phase observations, solving for uniform distribution on rectangular faults (Jiang & González, 2020). A geodetic inversion directly using the interferometric wrapped phase avoids any potential phase unwrapping error (Figure S6 in Supporting Information S1).…”
Section: Application Case: the 2011 Hawthorne Seismic Swarm (Nevada Usa)mentioning
confidence: 99%
“… Fault geometry for the 2011 Hawthorne seismic swarm. Image (a) indicates the fault plane with uniform slip retrieved by WGBIS (Jiang & González, 2020) from the wrapped interferograms, and the modeled phase and phase residuals are shown in Figure S8 in Supporting Information S1. In image (a), the green rectangle indicates the southern subfault which is active during the pre‐M4.6 stage, retrieved from RADARSAT‐2 interferogram 22 March 2011–15 April 2011; yellow rectangle indicates the northern subfault which is active during the co‐ and post‐M4.6 stages, retrieved from the RADARSAT‐2 interferogram 15 April 2011–26 June 2011, and the yellow triangle indicates the joint fault connecting two rectangle subfaults.…”
Section: Application Case: the 2011 Hawthorne Seismic Swarm (Nevada Usa)mentioning
How fault slip nucleates, grows, and eventually accelerates is a critical question to describe the driving mechanisms behind earthquakes and faulting phenomena. Our current understanding is consistent but cannot distinguish among various viable mechanisms to explain how fault slip initiates: dynamic triggering (Gomberg &
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