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Abstract The chemical composition of the deep continental crust is key to understanding the formation and evolution of the continental crust. Constraining the chemical composition of present‐day deep continental crust is, however, limited by indirect accessibility. This paper presents a modeling method for constraining deep crustal chemical structures from observed crustal seismic structures. We compiled a set of published composition models for the continental crust to construct functional relationships between seismic wave speed and major oxide content in the crust. Phase equilibria and compressional wave speeds (VP) for each composition model were calculated over a range of depths and temperatures of the deep crust. For conditions within the alpha(α)‐quartz stability field, robust functional relationships were obtained betweenVPand major oxide contents of the crust. Based on these relationships, observedVPof the deep crust can be inverted to chemical compositions for regions with given geotherms. We provide a MATLAB code for this process (CalcCrustComp). We apply this method to constrain compositions from deep crustalVPof global typical tectonic settings and the North China Craton (NCC). Our modeling results suggest that the lower crust in subduction‐related and rifting‐related tectonic settings may be more mafic than platforms/shields and orogens. The lowVPsignature in the deep crust of the NCC can be explained by intermediate crustal compositions, higher water contents, and/or higher temperatures. The chemical structure obtained by this method can serve as a reference model to further identify deep crustal features.
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Deep-learning optical flow for measuring velocity fields from experimental data
Deep learning-based optical flow (DLOF) extracts features in video frames with deep convolutional neural networks to estimate the inter-frame motions of objects. DLOF computes velocity fields more accurately than PIV for densely labeled systems.
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- Award ID(s):
- 2011846
- PAR ID:
- 10589923
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Soft Matter
- Volume:
- 20
- Issue:
- 36
- ISSN:
- 1744-683X
- Page Range / eLocation ID:
- 7246 to 7257
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d4sm00483c
