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A Bayesian method for incorporating self-similarity into earthquake slip inversions.

Amey, R. M. J. and Hooper, A. and Walters, R. J. (2018) 'A Bayesian method for incorporating self-similarity into earthquake slip inversions.', Journal of geophysical research : solid earth., 123 (7). pp. 6052-6071.


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 InSAR and 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 neighbouring 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.

Item Type:Article
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Publisher statement:© 2018. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Date accepted:05 June 2018
Date deposited:14 June 2018
Date of first online publication:17 July 2018
Date first made open access:No date available

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