Joint Inversion of Coseismic and Early Postseismic Slip to Optimize the Information Content in Geodetic Data: Application to the 2009 Mw6.3 L'Aquila Earthquake, Central Italy

Ragon, Théa; Sladen, Anthony; Blétery, Quentin; Vergnolle, Mathilde; Cavalié, Olivier; Avallone, A.; Balestra, Julien; Delouis, Bertrand

doi:10.1029/2018jb017053

Cited by 26 publications

(25 citation statements)

References 100 publications

(184 reference statements)

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“…Note that the surface displacements derived from the InSAR data contain between 8 and 9 days of post-seismic deformation, and that our GPS displacements are daily solutions, which might affect our modeling of the coseismic phase (e.g. Ragon et al, 2019a;Twardzik et al, 2019)…”

Section: Bayesian Samplingmentioning

confidence: 99%

Impact of topography on earthquake static slip estimates

et al. 2020

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Our understanding of earthquakes is limited by our knowledge, and our description, of the physics of the Earth. When solving for subsurface fault slip, it is common practice to assume minimum complexity for the Earth's characteristics such as topography, fault geometry and elastic properties. These characteristics are difficult to include in simulations and our knowledge of them is incomplete, leading many to believe that there is minimal advantage to be gained by accounting for these effects. However, 3D structure can be easily included with the newly-developed software package SPECFEM-X, and topography and bathymetry can be measured directly and are well known all over the Earth's surface. Accounting for topography thus seems to be an efficient strategy for incorporating accurate and extensive information into the inverse problem. Here, we explore the impact of topography on static slip estimates, with a particular focus on how topography may impact slip resolution on the fault. We also wonder if the influence of topography can be accounted for by simply using a receiver elevation correction. To this end, we analyze the 2015 M w 7.5 Gorkha, Nepal, and the 2010 M w 8.8 Maule, Chile, earthquakes within a Bayesian framework. The regions affected by these events represent two different types of topography. Chile, which contains both the Peru-Chile trench and the Andes mountains, has a greater elevation range and steeper gradients than Nepal, where the primary topographic feature is the Himalayan mountain range. Additionally, the slip of the continental Nepal event is well constrained, whereas observations are less informative in a subduction context. We show that topography has a non-negligible impact on inferred slip models. Our results suggest that the effect of topography on slip estimates increases with two main factors: poor observational constraints and high elevation gradients. In particular, we find that accounting for topography improves slip resolution where topographic gradients are large and the data is less informative: at depths for the Gorkha event, and near the trench for the Maule

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Section: Bayesian Samplingmentioning

confidence: 99%

Impact of topography on earthquake static slip estimates

et al. 2020

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“…In terms of postseismic slip inversions, Yano et al (2014) found that the first day of postseismic moment release of~6.0 × 10 17 Nm after the 2009 Mw 6.3 L'Aquila earthquake is nearly equal to the moment of 6.5 × 10 17 Nm released from the second day to 60 days (Cheloni et al, 2010). Ragon et al (2019) found that the peak early afterslip amplitude of the 2009 L'Aquila earthquake was up to three times greater than previously reported (Cheloni et al, 2014;D'Agostino et al, 2012). These studies highlight that the afterslip can be seriously underestimated, while the coseismic slip can be overestimated if direct measurements of the early postseismic deformation are absent.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method relies on both seismic and geodetic data, and the number of parameters in this method will increase significantly with extended temporal afterslip (Yano et al, 2014). Ragon et al (2019) modified this strategy by considering model uncertainties with the Bayesian method. They found an inverted peak coseismic slip was 30% higher than that reported in previous studies (Cheloni et al, 2014;D'Agostino et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Logarithmic Model Joint Inversion Method for Coseismic and Postseismic Slip: Application to the 2017 Mw 7.3 Sarpol Zahāb Earthquake, Iran

Liu

2019

JGR Solid Earth

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Interferometric synthetic aperture radar (InSAR) has become an important technique for studying earthquake cycle deformation. However, due to the limited satellite revisit time, it is often difficult to fully separate coseismic and postseismic slip from InSAR data. Nevertheless, accurately estimating spatiotemporal coseismic and postseismic fault slip distribution models is important for quantifying earthquake slip, understanding the kinematics of seismogenic faulting, and evaluating seismic hazards. Here, we develop a logarithmic model-based method (LogSIM) for the joint inversion of coseismic and postseismic fault slip using InSAR data from multiple platforms in different orbits. This method considers the nature of early postseismic slip following logarithmic decay. The coseismic slip, the decay time constant, and the decay amplitude of the logarithmic model can be jointly estimated from unwrapped interferograms without the need for InSAR time series calculations, thereby reducing the number of unknown parameters and stabilizing the inversion. The robustness of LogSIM is first validated with synthetic experiments. We then apply LogSIM to invert for the coseismic and postseismic slip models of the 2017 Mw 7.3 Sarpol Zahāb earthquake. We find that the maximum afterslip value and postseismic moment release during the first four months reported in previous studies are underestimated by~26% and~31%, respectively, due to the unavailability of the first five days of postseismic deformation. The estimated afterslip distribution spatially overlaps with the aftershocks in the updip region of the fault plane. LogSIM could potentially be extended to integrate different space geodetic data in earthquake cycle deformation modeling.

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“…C χ is the covariance matrix, which translates data and epistemic uncertainties into uncertainties on the inverted model m (Duputel et al, 2014;Bletery et al, 2016;Ragon et al, 2018;Ragon, Sladen, & Simons, 2019;Ragon, Sladen, Bletery, et al, 2019). Here, we only account for data uncertainties.…”

Section: Bayesian Inversion Of Rotation Poles and Interseismic Couplimentioning

confidence: 99%

Distribution of interseismic coupling along the North and East Anatolian Faults inferred from InSAR and GPS data

Blétery

Cavalié

Nocquet

et al. 2020

Preprint

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The North Anatolian Fault (NAF) is one of the most hazardous fault in the world. After decades of low seismicity, the M w 6.8 Elazig earthquake (January 24, 2020) has recently reminded us that the East Anatolian Fault (EAF) is also capable of producing large earthquakes. To better estimate the seismic hazard associated with these two faults, we jointly invert Interferometric Synthetic Aperture Radar (InSAR) and GPS data to image the spatial distribution of interseismic coupling along the eastern part of both the North and East Anatolian Faults. We perform the inversion in a Bayesian framework, enabling to estimate uncertainties on both long-term relative plate motion and coupling. We find that coupling is high and deep (0-20 km) on the NAF and heterogeneous and superficial (0-5 km) on the EAF. Our model predicts that the Elazig earthquake released between 200 and 250 years of accumulated moment, suggesting a bi-centennial recurrence time.

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Joint Inversion of Coseismic and Early Postseismic Slip to Optimize the Information Content in Geodetic Data: Application to the 2009 M_w6.3 L'Aquila Earthquake, Central Italy

Cited by 26 publications

References 100 publications

Impact of topography on earthquake static slip estimates

Impact of topography on earthquake static slip estimates

Logarithmic Model Joint Inversion Method for Coseismic and Postseismic Slip: Application to the 2017 Mw 7.3 Sarpol Zahāb Earthquake, Iran

Distribution of interseismic coupling along the North and East Anatolian Faults inferred from InSAR and GPS data

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