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- Title
Plate‐Locking, Uncertainty Estimation and Spatial Correlations Revealed With a Bayesian Model Selection Method: Application to the Central Chile Subduction Zone.
- Authors
Becerra‐Carreño, V.; Crempien, J. G. F.; Benavente, R.; Moreno, M.
- Abstract
Inversions of geodetic data are regularly used to estimate interseismic locking in subduction zones. However, the ill‐posed nature of these problems motivates us to include prior information, physically consistent with processes of the subduction seismic cycle. To deal with model instabilities, we present an inversion method to estimate both plate‐locking and model uncertainties by inverting Global Navigation Satellite System derived velocities based on a Bayesian model selection scheme. Our method allows us to impose positivity constraints via a multivariate folded‐normal distribution, with a specified covariance matrix. Model spatial correlations are explored and ranked to find models that best explain the observed data and for a better understanding of locking models. This approach searches for hyperparameters of the prior joint multivariate probability density function (PDF) of model parameters that minimize the Akaike Bayesian Information Criterion (ABIC). To validate our approach, we invert synthetic displacements from analytic models, yielding satisfactory results. We then apply the method to estimate the plate‐locking in Central Chile (28°–39°S) and its relation to the coseismic slip distribution of earthquakes with magnitudes Mw > 8.0, on the subduction zone since 2010. We also search among different prior PDFs for a single ductile‐fragile limit depth. Our results confirms a spatial correlation between locked asperities and the 2010 Mw 8.8 Maule and 2015 Mw 8.3 Illapel earthquake rupture zones. The robustness of our locking model shows potential to improve future seismic and tsunami hazard estimations. Plain Language Summary: Subduction zones accumulate large stresses between plate interface unions, which induce surface deformations, measurable by Global Positioning System (GPS) receivers. We use these GPS surface deformation rate data to infer the level of locking on subduction zones, namely, to infer the areas with highest and lowest amounts of accumulated stress. But, locking model solutions are not unique, due to several issues, such as long distances between stations and plate interface, as well as usually poor instrumental coverage and/or lack of GPS stations. We develop a method that determines the best locking model from a family of possible solutions, based on the resolution power the data can provide, penalizing overcomplicated locking models that cannot be explained by the data. The family of potential solutions include physical constraints, to obtain more plausible locking models. Our method estimates the uncertainty of our models, which provides the degree of confidence of the family of solutions. Our method has the advantage of ranking quantitatively each hypothesis, just from observations. We apply our approach to estimate locking on the Central Chile subduction segment, where big earthquakes have occurred (e.g., 2010 Maule and 2015 Illapel earthqaukes). We observe that our locking model peaks correlate well with areas where past earthquakes ruptured. Key Points: We propose a novel inversion approach to estimate plate‐locking, with positivity constraints via a multivariate folded normal distributionWe determine model hyperparameters by minimization of the Akaike Bayesian information criterionWe estimate spatial correlation parameters, model uncertainties and the seismogenic limit depth, all within the same Bayesian model selection method
- Subjects
MAULE (Chile : Region); SUBDUCTION zones; TSUNAMI warning systems; EARTHQUAKE magnitude; INVERSION (Geophysics); GLOBAL Positioning System; PROBABILITY density function; EARTHQUAKE zones; AKAIKE information criterion
- Publication
Journal of Geophysical Research. Solid Earth, 2022, Vol 127, Issue 10, p1
- ISSN
2169-9313
- Publication type
Article
- DOI
10.1029/2021JB023939