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Block Models, Cluster Analysis and Space Geodetic Data Needs to Better Estimate Fault Slip and Intra-Block Strain Rates in South

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Block Models, Cluster Analysis and Space Geodetic Data Needs to Better Estimate Fault Slip and Intra-Block Strain Rates in South Block Models, Cluster Analysis and! Space Geodetic Data Needs to Better Estimate ! Fault Slip and Intra-block Strain Rates in Southern California" Wayne Thatcher, Bob Simpson and Jim Savage U.S. Geological Survey, Menlo Park California Assumed California GPS Block Geometry (Parsons, Johnson et al, 2012) Block Geometry Derived from MoJave GPS Cluster Analysis 30 (Savage & Simpson, JGR, in review, 2012 ) 33 32 27 31 26 23 29 24 Central Walker Lane 20 SF 21 25 Bay Area 22 28 17 18 19 16 15 13 11 12 14 34 E MoJave 10 9 8 Region 4 3 7 6 1 2 5 Legend 0 55 110 220 UCERF3_GPS stations UCERF3_Block_model_2011_06_29 Kilometers But GPS Velocity Field Alone Constrains Locaon of Major AcRve Fault Boundaries >1400 edited velocity vectors Courtesy of T. Herring, MIT, to UCERF3 Schemac Illustraon of Cluster Analysis Method Vn Ve Cluster Analysis of GPS Vectors IdenRfies Major Tectonic Elements of California • No subJecRve assumpRon of block geometry • Simple, intuiRve analysis method • Major acRve tectonic features delineated: - faults of San Andreas system - MoJave Desert faults - Sierra Nevada-Great Valley microplate - Western boundary of Basin & Range - Cascadia subducRon zone boundary Cluster Analysis with Four GPS Clusters Determined Four San Francisco Bay Area Blocks Clearly IdenRfied Solely by Cluster Analysis See Graymer & Simpson G23B-931 Poster This A_ernoon Simpson, Thatcher & Savage GRL, 2012 Cluster Analysis Applied to MoJave Desert GPS with UCERF3 Block Boundaries (Grey Lines) & GPS Sites (Dots) Savage & Simpson, 2012, JGR, submi`ed 5 StasRcally Significant Clusters are Spaally Coherent Main Features Map of 5 Clusters • Cluster DistribuRon Similar to UCERF3 Block Geometry • However, Some Differences too • Garlock Fault Not “Seen” by Cluster Analysis • Existence of Smaller Blocks Not Precluded by Cluster Analysis • Large Block Rotaon in NEMD Does Not Contaminate Analysis Savage & Simpson, 2012 JGR, submi`ed Data Needs for Be`er Modeling of Steady-State Surface Velocity Field in Southern California • Be`er Precision in GPS VelociRes Everywhere (e.g. Be`er Define Clusters in Cluster Analysis) • Be`er Spaal Density for Measuring Intra-Block Strain (InSAR?, Sandwell, pers. comm. 2013) (More GPS sites too!) ADVANTAGES OF CLUSTER ANALYSIS Offers simple, visual, first-step reconnaissance to organize GPS velociRes Provides an obJecRve method for idenRfying major block boundaries Works best where Euler poles are distant and blocks ~translate StasRcal tests of block-like behavior of clusters build confidence in results Fault slip rates esRmated by difference in mean velocity between adJacent clusters Applicaon to other regional GPS data & including block rotaons now underway LIMITATIONS Only a relavely small number of stasRcally significant clusters, typically 5 or less Even random data can appear clustered, so use method with appropriate cauRon! Smaller blocks not precluded & cannot yet be confidently idenRfied by cluster analysis GPS precision & spaal density limit cluster resoluRon & can cause spurious clusters Large rotaons of blocks with nearby Euler poles may contaminate analysis Other space geodeRc data (e.g. InSAR) not yet incorporated into method San Francisco Bay Area Velocity Field Simpson, Thatcher & Savage GRL, 2012 San Francisco Bay Area Velocity Field 12 mm/yr 50 mm/yr Simpson, Thatcher & Savage GRL, 2012 Analysis with 5 StasRcally Significant Clusters Velocity Field Map Velocity Profiles N31˚W & N59˚E Savage & Simpson, 2012 JGR, submi`ed Cluster Analysis Applied to Central Walker Lane GPS with UCERF3 Block Boundaries (Grey Lines) UNR MAGNET GPS Net Savage & Simpson, 2012, in prep. Analysis with 4 StasRcally Significant Clusters Velocity Field Map Velocity Profiles N35˚W & N55˚E Savage & Simpson, 2012, in prep. .
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