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A new multiparameter database of Holocene and Pleistocene Cascades magmatism

A new multiparameter database of Holocene and Pleistocene Cascades magmatism. Lay Kuan Loh Advisor: Leif Karlstrom. Introduction. The Cascades Cascadia Subduction Zone Motivation. Cascades Range. Hildreth 2007. Subduction Zone. Syracuse and Abers , 2006. Motivation.

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A new multiparameter database of Holocene and Pleistocene Cascades magmatism

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  1. A new multiparameter database of Holocene andPleistocene Cascades magmatism Lay KuanLoh Advisor: Leif Karlstrom

  2. Introduction • The Cascades • CascadiaSubduction Zone • Motivation

  3. Cascades Range Hildreth 2007

  4. Subduction Zone Syracuse and Abers, 2006

  5. Motivation • Understanding spatial structure in Cascades vent distribution • Update and digitalize compilations of vents • Develop quantitative tools to assess patterns in high-dimensional datasets

  6. Database • Fields • Procedure for finding data

  7. Fields • Geochemistry • SiO2 • Al2O3 • FeO • MgO • Na2O • K2O • TiO2 • P2O5 • MnO • Tomography • Crystal Shear Perturbation • Upper Mantle P-Wave Perturbation • Heat Flow • Helium

  8. Finding Geochemistry List of vents to find Geochemistry for Compile corresponding USGS databases Search for matching map units within 0.5 decimal degrees of the vent No match Match exists Search NAVDAT for data points matching age and rock type within 0.5 decimal degrees of the vent Write geochemistry into database No match Match exists Search journal articles for geochemistry in nearby locations Write geochemistry into database

  9. Finding Heat Flow and Helium List of vents to find heat flow/helium for Compile corresponding heat flow/helium database Search for matching data point within 0.5 decimal degrees of the vent No match Match exists Leave field blank Write geochemistry into database

  10. Finding Crustal Shear Velocity and Upper Mantle P Perturbation • Interpolated from models by Porritt et al 2011and Schmandt et al 2010

  11. Spectral Clustering • Background on clustering • Clustering on vent locations and SiO2

  12. Spectral Clustering - Goals • Developing objective and quantitative methods for finding structures in Cascades vent data • We want to separate the vents v1,…,vn into groups with similar properties

  13. Spectral Clustering – Mathematical Tools • Measure of similarity • sij between points xi and xj • Similarity graphs • G=(V,E) • V: vertices • E: weighted edges of weight sij • Degree matrix D • dij= dii= ∑wij ifi=j • dij= 0 if i≠j • Normalized Graph Laplacian • Lsym = D-1/2(D-W)D1/2

  14. Spectral Clustering – Graph cut to partition G • Partition G into groups • Within groups, edges have high weights • Between groups, edges have low weights • Ng et al (2004) • Cluster on the first k eigenvectors of Lsym • Ordered by increasing eigenvalue • Get well-defined sets A1,…,Ak of G • k-means clustering on eigenvectors

  15. Parameters to consider • Factor for scaling the longitude • # eigenvectors • # clusters

  16. Finding best # clusters

  17. SiO2 versus distance from centroid

  18. Future Works • New database compilation

  19. Thank you

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