Source parameters of the 2014 Mw 6.1 South Napa earthquake estimated from the Sentinel 1A, COSMO-SkyMed and GPS data

Feng, Guangcai; Shan, Xinjian; Xu, Bing; Du, Yanan

doi:10.1016/j.tecto.2015.05.018

Cited by 30 publications

(17 citation statements)

References 11 publications

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“…We find that the von Karman solution extends high slip right to the surface, in contrast with the Laplacian solution and many other geodetic studies for this earthquake (Barnhart et al, ; Floyd et al, ; Guangcai et al, ; Melgar et al, ; Wei et al, ). Oversmoothing, as well as nonnegativity in the inversion and lack of data near the fault have been found to result in a lack of slip at the surface and apparent shallow‐slip deficit (Xu et al, ).…”

Section: Discussioncontrasting

confidence: 93%

“…We found peak magnitude of slip of ∼1.2 m at about 2‐km depth. This is similar in magnitude and location to InSAR and GPS studies by Guangcai et al (), Wei et al (), and Floyd et al () and the strong motion study by Zhang, Wang, et al (). Many studies also find a peak magnitude of 1.2 m, though find slip deeper at ∼5 km (Barnhart et al, ; Dreger et al, ; Melgar et al, ).…”

Section: Mw 60 Napa Valley Earthquake 2014supporting

confidence: 88%

“…By solving for the degree of smoothing with the hyperparameter α 2 we speculate that the von Karman regularization could potentially resolve slip at the surface better in the Napa Valley earthquake. However, we note that it is hard to directly compare specifics of slip solutions between published studies since different studies use different fault geometry, including one fault (Barnhart et al, ; Dreger et al, ; Melgar et al, ; Zhang, Wang, et al, ), two straight faults (Wei et al, ; Ji et al, ), or a more complicated curved fault geometry such as we used here (Floyd et al, ; Guangcai et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…The complexity of surface deformation in the north‐east, where two GPS points are poorly fit, is shown in the InSAR data (panel a of Figure ). This could be due to an unmodeled fault as suggested by Guangcai et al () or due to local surface effects and changes in elastic properties in the north‐east due to a change in lithology to Quaternary sediments. The modeling of these complexities is beyond the scope of this paper.…”

Section: Mw 60 Napa Valley Earthquake 2014mentioning

confidence: 91%

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A Bayesian Method for Incorporating Self‐Similarity Into Earthquake Slip Inversions

2018

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Distributions of coseismic slip help illuminate many properties of earthquakes, including fault geometry, stress changes, frictional properties, and potential future hazard. Slip inversions take observations and calculate slip at depth, but there are a number of commonly adopted assumptions such as minimizing the second spatial derivative of slip (the Laplacian) that have little physical basis and potentially bias the result. In light of growing evidence that fault slip shows fractal properties, we suggest that this information should be incorporated into slip inversions as a form of regularization, instead of Laplacian smoothing. We have developed a Bayesian approach to efficiently solve for slip incorporating von Karman regularization. In synthetic tests, our approach retrieves fractal slip better than Laplacian regularization, as expected, but even performs comparably, or better, when the input slip is not fractal. We apply this to the 2014 Mw 6.0 Napa Valley earthquake on a two‐segment fault using Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) data. We find the von Karman and Laplacian inversions give similar slip magnitude but in different locations, and the von Karman solution has much tighter confidence bounds on slip than the Laplacian solution. Differences in earthquake slip due to the regularization technique could have important implications for the interpretation and modeling of stress changes on the causative and neighboring faults. We therefore recommend that choice of regularization method should be routinely made explicit and justified and that von Karman regularization is a better default than Laplacian.

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Section: Discussioncontrasting

confidence: 93%

Section: Mw 60 Napa Valley Earthquake 2014supporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Mw 60 Napa Valley Earthquake 2014mentioning

confidence: 91%

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A Bayesian Method for Incorporating Self‐Similarity Into Earthquake Slip Inversions

2018

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“…Multi-temporal InSAR (MTInSAR) technique, an enhanced differential InSAR technique, has been widely applied in deformation mapping for urban surface, volcanos, mines and earthquakes [1][2][3][4][5][6][7]. In general, MTInSAR can be divided into three categories [8]: the single-master MTInSAR, such as PSInSAR, STUNS, and StaMPS-PS [1,[9][10][11]; the multi-master MTInSAR, for instance, SBAS, NSBAS, TCPInSAR, and StaMPS-SBAS [1,[12][13][14]; and the combination of the previous two categories [15,16].…”

Section: Introductionmentioning

confidence: 99%

Effects of External Digital Elevation Model Inaccuracy on StaMPS-PS Processing: A Case Study in Shenzhen, China

Feng

Peng

et al. 2017

Remote Sensing

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External Digital Elevation Models (DEMs) with different resolutions and accuracies cause different topographic residuals in differential interferograms of Multi-temporal InSAR (MTInSAR), especially for the phase-based StaMPS-PS. The PS selection and deformation parameter estimation of StaMPS-PS are closely related to the spatially uncorrected error, which is directly affected by external DEMs. However, it is still far from clear how the high resolution and accurate external DEM affects the results of the StaMPS-PS (e.g., PS selection and deformation parameter calculation) on different platforms (X band TerraSAR, C band ENVISAT ASAR and L band ALOS/PALSAR1). In this study, abundant synthetic tests are performed to assess the influences of external DEMs on parameter estimations, such as the mean deformation rate and the deformation time-series. Real SAR images, covering Shenzhen city in China, are also selected to analyze the PS selection and distribution as well as to validate the results of synthetic tests. The results show that the PS points selected by the 5 m TanDEM-X DEM are 10.32%, 4.25% and 0.34% more than those selected by the 30 m SRTM DEM at X, C and L bands SAR platforms, respectively, when a multi-look geocoding operation is adopted for X band in the SRTM DEM case. We also find that the influences of external DEMs on the mean deformation rate are not significant and are inversely proportional to the wavelength of the satellite platforms. The standard deviations of the mean deformation rate difference for the X, C and L bands are 0.54, 0.30 and 0.10 mm/year, respectively. Similarly, the influences of external DEMs on the deformation time-series estimation for the three platforms are also slight, except for local artifacts whose root-mean-square error (RMSE) ≥6 mm. Based on these analyses, some implications and suggestions for external DEMs on StaMPS-PS processing are discussed and provided.

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Stability of rapid finite‐fault inversion for the 2014 M_w6.1 South Napa earthquake

Zhang

Wang

Chen

2015

Geophysical Research Letters

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Local seismograms are useful for rapidly reconstructing kinematic finite‐fault sources, but the results often depend not only on the data coverage but also on uncertainties of parameters (e.g., hypocentral location and fault geometry) used as a priori information during the inversion. An automatic scheme was applied to offline tests for the 2014 South Napa earthquake. In the case of retrospective full‐waveform inversions, a network with station spacing of 10 km within the epicentral distance of 30 km is able to provide adequate stable key source parameters if the preestimated hypocenter and fault orientation are accurate of ±5 km and ±15°, respectively. In simulated real‐time inversions, the magnitude reaches Mw6.0 at 13 s, and the slip distribution matches that from the retrospective inversion at about 22–28 s after the origin time of the earthquake. These results are meaningful for estimating the lead time of a catastrophic seismic event.

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Source parameters of the 2014 Mw 6.1 South Napa earthquake estimated from the Sentinel 1A, COSMO-SkyMed and GPS data

Cited by 30 publications

References 11 publications

A Bayesian Method for Incorporating Self‐Similarity Into Earthquake Slip Inversions

A Bayesian Method for Incorporating Self‐Similarity Into Earthquake Slip Inversions

Effects of External Digital Elevation Model Inaccuracy on StaMPS-PS Processing: A Case Study in Shenzhen, China

Stability of rapid finite‐fault inversion for the 2014 M_w6.1 South Napa earthquake

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Source parameters of the 2014 Mw 6.1 South Napa earthquake estimated from the Sentinel 1A, COSMO-SkyMed and GPS data

Cited by 30 publications

References 11 publications

A Bayesian Method for Incorporating Self‐Similarity Into Earthquake Slip Inversions

A Bayesian Method for Incorporating Self‐Similarity Into Earthquake Slip Inversions

Effects of External Digital Elevation Model Inaccuracy on StaMPS-PS Processing: A Case Study in Shenzhen, China

Stability of rapid finite‐fault inversion for the 2014 Mw6.1 South Napa earthquake

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Stability of rapid finite‐fault inversion for the 2014 M_w6.1 South Napa earthquake