Abstract

Structural and tectonic models of ancient convergent margins commonly assume simple orthogonal convergence and collision; hence, they are easily represented in cross-sectional models. These simplified models, useful for predicting first-order relationships between geologic elements, often do not consider a margin-parallel component of relative motion. A growing body of evidence suggests that during oblique convergence, subduction-related margin-parallel strike-slip shear zones (MPSZs) play an important role in the evolution of many convergent margins.MPSZs are probably most important along margins of oblique convergence characterized by shallow subduction, an overriding plate comprised of continental crust, and an absence of salients that may inhibit margin-parallel motion. Recognition of evidence for oblique convergence and MPSZs, as preserved in the rock record, is the next step in refining convergent plate margin tectonic models. This is particularly important with respect to our understanding of continental margin kinematics in the past, for which there exist few data to constrain plate motions. The formation and geometry of elements within MPSZs depends on structural level and location (i.e., distance from the trench) within the overriding plate. Detailed structural and kinematic analysis across presumed ancient margins may reveal subduction-related MPSZs. Knowledge of MPSZs active along the plate décollement as well as within the overriding plate may allow limits to be placed on the direction, time, and magnitude of the translation of upper-plate slivers. Documentation of MPSZs and incorporation of relevant motion and age data into multi-dimensional models, those which consider more than a classical cross-orogen section, may greatly advance models of Mesozoic (and older) convergent plate-margin evolution.KeywordsShear ZoneContinental CrustSubduction ZoneOceanic CrustCrustal LevelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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