2021
DOI: 10.1029/2020jb021141
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An EPIC Tikhonov Regularization: Application to Quasi‐Static Fault Slip Inversion

Abstract: Common approaches to spatial regularization are susceptible to unstable biases in 10 estimates of subsurface fault slip. 11• We introduce a novel spatially-variable regularization scheme called EPIC Tikhonov. 12• Use of EPIC Tikhonov results in robust estimates of fault slip, in terms of stability, 13 bias and ease of uncertainty analysis.

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Cited by 16 publications
(13 citation statements)
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“…This means that target cannot be predicted by the uncertainties. We attribute this to the fact that in these shallow areas, the sensitivity is very low, as other works have observed (e.g., Ortega‐Culaciati et al., 2021), that is, if we change the amplitude of a parameter, it is not strongly affected in the data. Which means that the areas near the trench intrinsically have poor resolution in their solution.…”
Section: Discussionsupporting
confidence: 59%
“…This means that target cannot be predicted by the uncertainties. We attribute this to the fact that in these shallow areas, the sensitivity is very low, as other works have observed (e.g., Ortega‐Culaciati et al., 2021), that is, if we change the amplitude of a parameter, it is not strongly affected in the data. Which means that the areas near the trench intrinsically have poor resolution in their solution.…”
Section: Discussionsupporting
confidence: 59%
“…(2020). We attribute such disagreements to methodological differences, as we additionally estimate surface regional strain rates, account for epistemic uncertainties, and use the spatially variable EPIC Tikhonov regularization that improves the along dip resolution of the inferred back‐slip distribution (Ortega‐Culaciati et al., 2021).…”
Section: Discussionmentioning
confidence: 99%
“…Here, we set prior information on a quantity h = Hm representing some characteristics of back‐slip and regional motion model parameters, constraining the values of h to be close to h o = 0 with covariance matrix C h . We use the EPIC Tikhonov regularization to define a spatially variable smoothing prior for back‐slip, improving stability, resolution and interpretability of slip estimates, particularly along the dip direction (Ortega‐Culaciati et al., 2021). The EPIC counterbalances the spatial heterogeneity of the constraints on back‐slip provided by GNSS observations, by determining variances of prior information ( C h ) based on a chosen form of the structure of the posterior covariance matrix ()trueCboldm $\left({\tilde{\mathbf{C}}}_{\mathbf{m}}\right)$.…”
Section: Methodsmentioning
confidence: 99%
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