Introduction. The Supplement shows results of locking using a range of smoothing parameters α, and checkerboard tests.
|
|
- Winifred Snow
- 7 years ago
- Views:
Transcription
1 Auxiliary Material Submission for Paper 2014JB Robert McCaffrey Portland State University Inter-seismic locking on the Hikurangi subduction zone: Uncertainties from slow-slip events Introduction The Supplement shows results of locking using a range of smoothing parameters α, and checkerboard tests. Also given are the GPS velocity fields used. Velocity fields: These have been rotated into the Australian reference frame JB ts01.txt Survey mode GPS velocity field 1.1 Column "Site", Site name 1.2 Column "Long.", degrees, longitude of site, east of Greenwich. 1.3 Column "Lat.", degrees, latitude of site, north of equator. 1.4 Column "Ve", mm/yr, East velocity of site 1.4 Column "Se", mm/yr, East velocity uncertainty 1.5 Column "Vn", mm/yr, North velocity of site 1.6 Column "Sn", mm/yr, North velocity uncertainty JB ts02.txt Continuous GPS velocity field 2.1 Column "Site", Site name 2.2 Column "Long.", degrees, longitude of site, east of Greenwich. 2.3 Column "Lat.", degrees, latitude of site, north of equator. 2.4 Column "Ve", mm/yr, East velocity of site 2.5 Column "Vn", mm/yr, North velocity of site 2.6 Column "Vz", mm/yr, Up velocity of site 2.7 Column "Se1", mm/yr, East velocity uncertainty (Williams method) 2.8 Column "Sn1", mm/yr, North velocity uncertainty (Williams method) 2.9 Column "Sz1", mm/yr, Up velocity uncertainty (Williams method) 2.10 Column "Se2", mm/yr, East velocity uncertainty (Hackl method) 2.11 Column "Sn2", mm/yr, North velocity uncertainty (Hackl method) 2.12 Column "Sz2", mm/yr, Up velocity uncertainty (Hackl method) 2.13 Column "Start_Yr", Year, Start time of GPS site 2.14 Column "End_Yr", Year, End time of GPS site 2.15 Column "Duration_Yr",Years, Duration of GPS site JB ts03.txt Continuous GPS velocity field 3.1 Column "Site", Site name 3.2 Column "Duration",Years, Duration of GPS site 3.3 Column "Model_T",Years, Duration of Monte Carlo simulation 3.4 Column "Se", mm/yr, East velocity uncertainty (Monte Carlo method) 3.5 Column "Sn", mm/yr, North velocity uncertainty (Monte Carlo method) 3.6 Column "Sz", mm/yr, Up velocity uncertainty (Monte Carlo method) 3.7 Column "Ve", mm/yr, East velocity of site due to transients 3.8 Column "Vn", mm/yr, North velocity of site due to transients
2 3.9 Column "Vz", mm/yr, Up velocity of site due to transients JB ts04.txt Offsets from transients 4.1 Column "Site", Site name 4.2 Column "Start_Yr", Year, Start time of GPS site 4.3 Column "End_Yr", Year, End time of GPS site 4.4 Column "Duration_Yr",Years, Duration of GPS site For each site: 4.5 Time in Years 4.6 East offset, mm 4.7 North offset, mm 4.8 Up offset, mm JB fs01.eps (Figure S-1). Data misfit for given smoothing exponents. Black dots are for Gaussian model and red for Exponential model. Labels at bottom are model names JB fs02.eps (Figure S-2) Locking results for models with different smoothing exponents. In each pair, at left is Exponential model and at right is Gaussian model. Labels give the model name ni5x, followed by data chi-squared misfit and moment rate in Nm/yr. For models ni5h and ni56, α = 10^4, for models ni5i and ni59, α = 10^ JB fs03.eps (Figure S-3a) For these tests a velocity field was generated from the locking distribution shown in the top panel. Random errors were added to the velocities at the sites according to the uncertainty in the observed velocities. The simulated data were then inverted for the slip distribution. The two test cases use the same distribution of patches but the locking fraction was reversed (0 = free-slip or 1 = fully locked). The middle figure shows the result when the starting model was a fully locked fault and in the bottom figure the starting model was a fully free-slip fault. This was done to see if the data or the moment-damping constraint drove a patch to be free-slip. The results are quite similar for the two starting models. These tests show that locking in the up-dip part of the fault is unresolved and the deeper parts of the fault have lower resolution JB fs04.eps (Figure S-3b) For these tests a velocity field was generated from the locking distribution shown in the top panel. Random errors were added to the velocities at the sites according to the uncertainty in the observed velocities. The simulated data were then inverted for the slip distribution. The two test cases use the same distribution of patches but the locking fraction was reversed (0 = free-slip or 1 = fully locked). The middle figure shows the result when the starting model was a fully locked fault and in the bottom figure the starting model was a fully free-slip fault. This was done to see if the data or the moment-damping constraint drove a patch to be free-slip. The results are quite similar for the two starting models. These tests show that locking in the up-dip part of the fault is unresolved and the deeper parts of the fault have lower resolution.
3 Supplementary Material for: McCaffrey, R., Inter-seismic locking on the Hikurangi subduction zone: Uncertainties from slow-slip events, JGR submitted. See also the README file Smoothing tests: Figure S-1. Data misfit for given smoothing exponents. Black dots are for Gaussian model and red for Exponential model. Labels at bottom are model names. Figure S-2. Locking results for models with varying smoothing exponents. In each pair, at left is Exponential model and at right is Gaussian model. Labels give the model name ni5x, data chi-squared misfit and moment rate in Nm/yr. For models ni5h and ni56, α = 10 4, and for models ni5i and ni59, α = Checkerboard tests: Figure S-3. For these tests a velocity field was generated from the locking distribution shown in the top panel. Random errors were added to the velocities at the sites according to the uncertainty in the observed velocities. The simulated data were then inverted for the slip distribution. The two test cases use the same distribution of patches but the locking fraction was reversed (0 = free-slip or 1 = fully locked). The middle figure shows the result when the starting model was a fully locked fault and in the bottom figure the starting model was a fully free-slip fault. This was done to see if the data or the moment-damping constraint drove a patch to be free-slip. The results are quite similar for the two starting models. These tests show that locking in the up-dip part of the fault is unresolved and the deeper parts of the fault have lower resolution. Test of Error Models: To compare the uncertainties estimated by the Hackl et al. [2011], Williams [2003b] and Monte Carlo methods, I generated a 10-year long time series that had 40 evenly spaced, random offsets. The offsets followed a Gaussian distribution with variance of 1 mm 2 and a mean of zero. The Williams predicted velocity variance is p σ d 2 / T where p = 40/(10*365.25), σ d 2 =1.0 mm 2, and T = 10*365.25, giving σ = 0.63 mm/yr. For 3 test time series generated with those offsets, the Hackl method gave uncertatintes of 0.62, 0.43 and 0.57 mm/yr. The Monte Carlo method for the same 3 time series gave standard deviation of the slopes of 0.62, 0.65 and 0.56 mm/yr. Offsets: All estimated offsets for slow-slip, volcano deformation and earthquakes are given in an accompanying file 2014JB ts04.txt. 1
4 Velocity fields: These have been rotated into the Australian reference frame JB ts01.txt Survey mode GPS velocity field 1.1 Column "Site", Site name 1.2 Column "Long.", degrees, longitude of site, east of Greenwich. 1.3 Column "Lat.", degrees, latitude of site, north of equator. 1.4 Column "Ve", mm/yr, East velocity of site 1.4 Column "Se", mm/yr, East velocity uncertainty 1.5 Column "Vn", mm/yr, North velocity of site 1.6 Column "Sn", mm/yr, North velocity uncertainty JB ts02.txt Continuous GPS velocity field 2.1 Column "Site", Site name 2.2 Column "Long.", degrees, longitude of site, east of Greenwich. 2.3 Column "Lat.", degrees, latitude of site, north of equator. 2.4 Column "Ve", mm/yr, East velocity of site 2.5 Column "Vn", mm/yr, North velocity of site 2.6 Column "Vz", mm/yr, Up velocity of site 2.7 Column "Se1", mm/yr, East velocity uncertainty (Williams method) 2.8 Column "Sn1", mm/yr, North velocity uncertainty (Williams method) 2.9 Column "Sz1", mm/yr, Up velocity uncertainty (Williams method) 2.10 Column "Se2", mm/yr, East velocity uncertainty (Hackl method) 2.11 Column "Sn2", mm/yr, North velocity uncertainty (Hackl method) 2.12 Column "Sz2", mm/yr, Up velocity uncertainty (Hackl method) 2.13 Column "Start_Yr", Year, Start time of GPS site 2.14 Column "End_Yr", Year, End time of GPS site 2.15 Column "Duration_Yr",Years, Duration of GPS site JB ts03.txt Continuous GPS velocity field 3.1 Column "Site", Site name 3.2 Column "Duration",Years, Duration of GPS site 3.3 Column "Model_T",Years, Duration of Monte Carlo simulation 3.4 Column "Se", mm/yr, East velocity uncertainty (Monte Carlo method) 3.5 Column "Sn", mm/yr, North velocity uncertainty (Monte Carlo method) 3.6 Column "Sz", mm/yr, Up velocity uncertainty (Monte Carlo method) 3.7 Column "Ve", mm/yr, East velocity of site due to transients 3.8 Column "Vn", mm/yr, North velocity of site due to transients 3.9 Column "Vz", mm/yr, Up velocity of site due to transients JB ts04.txt Offsets from transients 4.1 Column "Site", Site name 4.2 Column "Start_Yr", Year, Start time of GPS site 4.3 Column "End_Yr", Year, End time of GPS site 4.4 Column "Duration_Yr",Years, Duration of GPS site 2
5 For each site: 4.5 Time in Years 4.6 East offset, mm 4.7 North offset, mm 4.8 Up offset, mm JB fs01.eps (Figure S-1). Data misfit for given smoothing exponents. Black dots are for Gaussian model and red for Exponential model. Labels at bottom are model names JB fs02.eps (Figure S-2) Locking results for models with different smoothing exponents. In each pair, at left is Exponential model and at right is Gaussian model. Labels give the model name ni5x, followed by data chi-squared misfit and moment rate in Nm/yr. For models ni5h and ni56, α = 10^4, for models ni5i and ni59, α = 10^ JB fs03.eps (Figure S-3a) For these tests a velocity field was generated from the locking distribution shown in the top panel. Random errors were added to the velocities at the sites according to the uncertainty in the observed velocities. The simulated data were then inverted for the slip distribution. The two test cases use the same distribution of patches but the locking fraction was reversed (0 = free-slip or 1 = fully locked). The middle figure shows the result when the starting model was a fully locked fault and in the bottom figure the starting model was a fully free-slip fault. This was done to see if the data or the moment-damping constraint drove a patch to be freeslip. The results are quite similar for the two starting models. These tests show that locking in the up-dip part of the fault is unresolved and the deeper parts of the fault have lower resolution JB fs04.eps (Figure S-3b) For these tests a velocity field was generated from the locking distribution shown in the top panel. Random errors were added to the velocities at the sites according to the uncertainty in the observed velocities. The simulated data were then inverted for the slip distribution. The two test cases use the same distribution of patches but the locking fraction was reversed (0 = free-slip or 1 = fully locked). The middle figure shows the result when the starting model was a fully locked fault and in the bottom figure the starting model was a fully free-slip fault. This was done to see if the data or the moment-damping constraint drove a patch to be freeslip. The results are quite similar for the two starting models. These tests show that locking in the up-dip part of the fault is unresolved and the deeper parts of the fault have lower resolution. 3
Exploring plate motion and deformation in California with GPS
Exploring plate motion and deformation in California with GPS Student worksheet Cate Fox-Lent, UNAVCO master teacher; Andy Newman, Georgia Institute of Technology; Shelley Olds, UNAVCO; and revised by
More informationFinding location and velocity data for PBO GPS stations
Finding location and velocity data for PBO GPS stations Original activity by Vince Cronin (Baylor University). Revisions by Beth Pratt-Sitaula (UNAVCO). Analyzing the velocities recorded at different GPS
More informationMultiple Optimization Using the JMP Statistical Software Kodak Research Conference May 9, 2005
Multiple Optimization Using the JMP Statistical Software Kodak Research Conference May 9, 2005 Philip J. Ramsey, Ph.D., Mia L. Stephens, MS, Marie Gaudard, Ph.D. North Haven Group, http://www.northhavengroup.com/
More information1 Introduction. External Grant Award Number: 04HQGR0038. Title: Retrieval of high-resolution kinematic source parameters for large earthquakes
External Grant Award Number: 04HQGR0038 Title: Retrieval of high-resolution kinematic source parameters for large earthquakes Author: Hong Kie Thio URS Group Inc. 566 El Dorado Street, 2 nd floor Pasadena,
More informationChapter 3 RANDOM VARIATE GENERATION
Chapter 3 RANDOM VARIATE GENERATION In order to do a Monte Carlo simulation either by hand or by computer, techniques must be developed for generating values of random variables having known distributions.
More informationReading GPS Time Series Plots Worksheet
Reading GPS Time Series Plots Worksheet By: Roger Groom and Cate Fox-Lent, UNAVCO Master Teachers in-residence, Shelley Olds, UNAVCO The Global Positioning System, GPS, is used to study the Earth, how
More informationAssignment 2: Option Pricing and the Black-Scholes formula The University of British Columbia Science One CS 2015-2016 Instructor: Michael Gelbart
Assignment 2: Option Pricing and the Black-Scholes formula The University of British Columbia Science One CS 2015-2016 Instructor: Michael Gelbart Overview Due Thursday, November 12th at 11:59pm Last updated
More informationEvaluating Trading Systems By John Ehlers and Ric Way
Evaluating Trading Systems By John Ehlers and Ric Way INTRODUCTION What is the best way to evaluate the performance of a trading system? Conventional wisdom holds that the best way is to examine the system
More informationEvaluating System Suitability CE, GC, LC and A/D ChemStation Revisions: A.03.0x- A.08.0x
CE, GC, LC and A/D ChemStation Revisions: A.03.0x- A.08.0x This document is believed to be accurate and up-to-date. However, Agilent Technologies, Inc. cannot assume responsibility for the use of this
More informationProgram for statistical comparison of histograms
ROOT 3, Saas-Fee, March, Program for statistical comparison of histograms S. Bityukov (IHEP, INR), N. Tsirova (NPI MSU) Scope Introduction Statistical comparison of two histograms Normalized significance
More informationMCMC-Based Assessment of the Error Characteristics of a Surface-Based Combined Radar - Passive Microwave Cloud Property Retrieval
MCMC-Based Assessment of the Error Characteristics of a Surface-Based Combined Radar - Passive Microwave Cloud Property Retrieval Derek J. Posselt University of Michigan Jay G. Mace University of Utah
More informationCHAPTER 12 EXAMPLES: MONTE CARLO SIMULATION STUDIES
Examples: Monte Carlo Simulation Studies CHAPTER 12 EXAMPLES: MONTE CARLO SIMULATION STUDIES Monte Carlo simulation studies are often used for methodological investigations of the performance of statistical
More informationContouring and Advanced Visualization
Contouring and Advanced Visualization Contouring Underlay your oneline with an image Geographic Data Views Auto-created geographic data visualization Emphasis of Display Objects Make specific objects standout
More informationRing of Fire. (15 minutes) Earthquakes and volcanoes occur in relationship to each other.
Ring of Fire Lesson Concept Link Earthquakes and volcanoes occur in relationship to each other. Lesson 6.12 develops concepts about preparation for earthquakes in terms of home or school damage or lack
More informationPlotting Earthquake Epicenters an activity for seismic discovery
Plotting Earthquake Epicenters an activity for seismic discovery Tammy K Bravo Anne M Ortiz Plotting Activity adapted from: Larry Braile and Sheryl Braile Department of Earth and Atmospheric Sciences Purdue
More informationTutorial on Using Excel Solver to Analyze Spin-Lattice Relaxation Time Data
Tutorial on Using Excel Solver to Analyze Spin-Lattice Relaxation Time Data In the measurement of the Spin-Lattice Relaxation time T 1, a 180 o pulse is followed after a delay time of t with a 90 o pulse,
More informationIntroduction Pricing Effects Greeks Summary. Vol Target Options. Rob Coles. February 7, 2014
February 7, 2014 Outline 1 Introduction 2 3 Vega Theta Delta & Gamma Hedge P& L Jump sensitivity The Basic Idea Basket split between risky asset and cash Chose weight of risky asset w to keep volatility
More informationGeostatistics Exploratory Analysis
Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Master of Science in Geospatial Technologies Geostatistics Exploratory Analysis Carlos Alberto Felgueiras cfelgueiras@isegi.unl.pt
More informationPacific Sea Level Monitoring Project
Record 2015/04 GeoCat 82325 Pacific Sea Level Monitoring Project CGPS Coordinate Time Series Analysis Report Jia, M., Dawson, J., Twilley, B. and Hu, G. APPLYING GEOSCIENCE TO AUSTRALIA S MOST IMPORTANT
More informationA Case Study in Software Enhancements as Six Sigma Process Improvements: Simulating Productivity Savings
A Case Study in Software Enhancements as Six Sigma Process Improvements: Simulating Productivity Savings Dan Houston, Ph.D. Automation and Control Solutions Honeywell, Inc. dxhouston@ieee.org Abstract
More informationPrecalculus REVERSE CORRELATION. Content Expectations for. Precalculus. Michigan CONTENT EXPECTATIONS FOR PRECALCULUS CHAPTER/LESSON TITLES
Content Expectations for Precalculus Michigan Precalculus 2011 REVERSE CORRELATION CHAPTER/LESSON TITLES Chapter 0 Preparing for Precalculus 0-1 Sets There are no state-mandated Precalculus 0-2 Operations
More informationCalculating P-Values. Parkland College. Isela Guerra Parkland College. Recommended Citation
Parkland College A with Honors Projects Honors Program 2014 Calculating P-Values Isela Guerra Parkland College Recommended Citation Guerra, Isela, "Calculating P-Values" (2014). A with Honors Projects.
More informationTracking Project Progress
L E S S O N 2 Tracking Project Progress Suggested lesson time 45-55 minutes Lesson objectives To begin tracking an active project, you will: a b c Modify the environment for tracking. You will use the
More informationDemonstration of Data Analysis using the Gnumeric Spreadsheet Solver to Estimate the Period for Solar Rotation
Demonstration of Data Analysis using the Gnumeric Spreadsheet Solver to Estimate the Period for Solar Rotation Ron Larham Hart Plain Institute for Studies Introduction This paper serves two purposes, the
More informationThis ReadMe file describes the changes in the release of Multiframe 17.00 V8i.
Multiframe 17.00 V8i ReadMe 16 September 2013 This ReadMe file describes the changes in the release of Multiframe 17.00 V8i. System Requirements: 32-bit versions will run on Windows XP/Vista/7, and 64-bit
More informationLesson 7 - Creating Animation II
Lesson 7 - Creating Animation II A. Motion-Tweened Animation With motion tweening, you can easily create motion effects for the objects in your Flash movies. Kites flying, balls bouncing, rocks rolling
More informationWorking Model 2D Exercise Problem 14.111. ME 114 Vehicle Design Dr. Jose Granda. Performed By Jeffrey H. Cho
Working Model 2D Exercise Problem 14.111 ME 114 Vehicle Design Dr. Jose Granda Performed By Jeffrey H. Cho Table of Contents Problem Statement... 1 Simulation Set-Up...2 World Settings... 2 Gravity...
More informationSimulation Exercises to Reinforce the Foundations of Statistical Thinking in Online Classes
Simulation Exercises to Reinforce the Foundations of Statistical Thinking in Online Classes Simcha Pollack, Ph.D. St. John s University Tobin College of Business Queens, NY, 11439 pollacks@stjohns.edu
More informationName: Date: Class: Finding Epicenters and Measuring Magnitudes Worksheet
Example Answers Name: Date: Class: Finding Epicenters and Measuring Magnitudes Worksheet Objective: To use seismic data and an interactive simulation to triangulate the location and measure the magnitude
More informationOBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS
OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS CLARKE, Stephen R. Swinburne University of Technology Australia One way of examining forecasting methods via assignments
More informationPart 1 : 07/27/10 21:30:31
Question 1 - CIA 593 III-64 - Forecasting Techniques What coefficient of correlation results from the following data? X Y 1 10 2 8 3 6 4 4 5 2 A. 0 B. 1 C. Cannot be determined from the data given. D.
More informationGetting Land Survey Vertical & Horizontal Control via the Internet
Getting Land Survey Vertical & Horizontal Control via the Internet https://www.auroragov.org aka: City of Aurora home page ROLL OVER City Hall Dropdown will appear 1 PICK Maps In the Info List 2 PICK Mapping
More informationHow to compute Random acceleration, velocity, and displacement values from a breakpoint table.
How to compute Random acceleration, velocity, and displacement values from a breakpoint table. A random spectrum is defined as a set of frequency and amplitude breakpoints, like these: 0.050 Acceleration
More informationNormality Testing in Excel
Normality Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com
More informationDeflection Calculation of RC Beams: Finite Element Software Versus Design Code Methods
Deflection Calculation of RC Beams: Finite Element Software Versus Design Code Methods G. Kaklauskas, Vilnius Gediminas Technical University, 1223 Vilnius, Lithuania (gintaris.kaklauskas@st.vtu.lt) V.
More informationGaussian Processes to Speed up Hamiltonian Monte Carlo
Gaussian Processes to Speed up Hamiltonian Monte Carlo Matthieu Lê Murray, Iain http://videolectures.net/mlss09uk_murray_mcmc/ Rasmussen, Carl Edward. "Gaussian processes to speed up hybrid Monte Carlo
More informationInstituto de Geofísica, Universidad Nacional Autónoma de México, México, D.F., México
The silent earthquake of 2002 in the Guerrero seismic gap, Mexico (Mw=7.6): inversion of slip on the plate interface and some implications (Submitted to Earth and Planetary Science Letters) A. Iglesias
More informationSTT315 Chapter 4 Random Variables & Probability Distributions KM. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables
Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables Discrete vs. continuous random variables Examples of continuous distributions o Uniform o Exponential o Normal Recall: A random
More informationBill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1
Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1 Calculate counts, means, and standard deviations Produce
More informationT Sentry 4 Multi-Point Digital Alarm Instruction Manual
T Sentry 4 Multi-Point Digital Alarm Instruction Manual Introduction The T Sentry4 (TS4) is a microprocessor-based temperature monitoring and alarm device with user programmable high-low alarm setpoints
More informationLaminar Flow in a Baffled Stirred Mixer
Laminar Flow in a Baffled Stirred Mixer Introduction This exercise exemplifies the use of the rotating machinery feature in the CFD Module. The Rotating Machinery interface allows you to model moving rotating
More informationIntroduction to Statistical Computing in Microsoft Excel By Hector D. Flores; hflores@rice.edu, and Dr. J.A. Dobelman
Introduction to Statistical Computing in Microsoft Excel By Hector D. Flores; hflores@rice.edu, and Dr. J.A. Dobelman Statistics lab will be mainly focused on applying what you have learned in class with
More informationInternet Appendix to False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas
Internet Appendix to False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas A. Estimation Procedure A.1. Determining the Value for from the Data We use the bootstrap procedure
More informationMeasuring Line Edge Roughness: Fluctuations in Uncertainty
Tutor6.doc: Version 5/6/08 T h e L i t h o g r a p h y E x p e r t (August 008) Measuring Line Edge Roughness: Fluctuations in Uncertainty Line edge roughness () is the deviation of a feature edge (as
More informationMonte Carlo Simulation. SMG ITS Advanced Excel Workshop
Advanced Excel Workshop Monte Carlo Simulation Page 1 Contents Monte Carlo Simulation Tutorial... 2 Example 1: New Marketing Campaign... 2 VLOOKUP... 5 Example 2: Revenue Forecast... 6 Pivot Table... 8
More informationCommon Core Unit Summary Grades 6 to 8
Common Core Unit Summary Grades 6 to 8 Grade 8: Unit 1: Congruence and Similarity- 8G1-8G5 rotations reflections and translations,( RRT=congruence) understand congruence of 2 d figures after RRT Dilations
More informationMagnitude 7.2 GUERRERO, MEXICO
A powerful magnitude-7.2 earthquake shook central and southern Mexico on Friday. The earthquake occurred at a depth of 24 km (15 miles). Its epicenter was in the western state of Guerrero, near the seaside
More informationDetermining Measurement Uncertainty for Dimensional Measurements
Determining Measurement Uncertainty for Dimensional Measurements The purpose of any measurement activity is to determine or quantify the size, location or amount of an object, substance or physical parameter
More informationWhere in the World Are All the Earthquakes?
Curry School of Education, University of Virginia www.teacherlink.org/content/science/ Where in the World Are All the Earthquakes? In this activity, students go to the United States Geological Survey (USGS)
More informationCHI-SQUARE: TESTING FOR GOODNESS OF FIT
CHI-SQUARE: TESTING FOR GOODNESS OF FIT In the previous chapter we discussed procedures for fitting a hypothesized function to a set of experimental data points. Such procedures involve minimizing a quantity
More informationCalculating VaR. Capital Market Risk Advisors CMRA
Calculating VaR Capital Market Risk Advisors How is VAR Calculated? Sensitivity Estimate Models - use sensitivity factors such as duration to estimate the change in value of the portfolio to changes in
More informationManual for Reclamation Fault-Based Probabilistic Seismic Hazard Analysis Software
RECLAMATION West Managing Water in the Report DSO-07-01 Manual for Reclamation Fault-Based Probabilistic Seismic Hazard Analysis Software Dam Safety Technology Development Program U.S. Department of the
More informationThe Heston Model. Hui Gong, UCL http://www.homepages.ucl.ac.uk/ ucahgon/ May 6, 2014
Hui Gong, UCL http://www.homepages.ucl.ac.uk/ ucahgon/ May 6, 2014 Generalized SV models Vanilla Call Option via Heston Itô s lemma for variance process Euler-Maruyama scheme Implement in Excel&VBA 1.
More informationSubjects: Fourteen Princeton undergraduate and graduate students were recruited to
Supplementary Methods Subjects: Fourteen Princeton undergraduate and graduate students were recruited to participate in the study, including 9 females and 5 males. The mean age was 21.4 years, with standard
More informationimproved understanding of secular and transient deformation in Southern California and loading of How can the CRM contribute to seismogenic faults?
How can the CRM contribute to improved understanding of secular and transient deformation in Southern California and loading of seismogenic faults? Yuri Fialko Institute of Geophysics and Planetary Physics
More informationProject 3. Trade stocks to make as much money as possible.
Project. Trade stocks to make as much money as possible. CEE 2L. Uncertainty, Design, and Optimization Department of Civil & Environmental Engineering Duke University Henri P. Gavin and Steve Lattanzio,
More information12.510 Introduction to Seismology Spring 2008
MIT OpenCourseWare http://ocw.mit.edu 12.510 Introduction to Seismology Spring 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 04/30/2008 Today s
More informationData representation and analysis in Excel
Page 1 Data representation and analysis in Excel Let s Get Started! This course will teach you how to analyze data and make charts in Excel so that the data may be represented in a visual way that reflects
More information5-Axis Test-Piece Influence of Machining Position
5-Axis Test-Piece Influence of Machining Position Michael Gebhardt, Wolfgang Knapp, Konrad Wegener Institute of Machine Tools and Manufacturing (IWF), Swiss Federal Institute of Technology (ETH), Zurich,
More informationSTA 4273H: Statistical Machine Learning
STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct
More informationDescriptive Statistics
Y520 Robert S Michael Goal: Learn to calculate indicators and construct graphs that summarize and describe a large quantity of values. Using the textbook readings and other resources listed on the web
More informationUNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010
UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 COURSE: POM 500 Statistical Analysis, ONLINE EDITION, Fall 2010 Prerequisite: Finite Math
More informationitesla Project Innovative Tools for Electrical System Security within Large Areas
itesla Project Innovative Tools for Electrical System Security within Large Areas Samir ISSAD RTE France samir.issad@rte-france.com PSCC 2014 Panel Session 22/08/2014 Advanced data-driven modeling techniques
More informationLAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING
LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING In this lab you will explore the concept of a confidence interval and hypothesis testing through a simulation problem in engineering setting.
More informationDecember 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS
December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in two-dimensional space (1) 2x y = 3 describes a line in two-dimensional space The coefficients of x and y in the equation
More informationanimation animation shape specification as a function of time
animation animation shape specification as a function of time animation representation many ways to represent changes with time intent artistic motion physically-plausible motion efficiency control typically
More informationSubspace Analysis and Optimization for AAM Based Face Alignment
Subspace Analysis and Optimization for AAM Based Face Alignment Ming Zhao Chun Chen College of Computer Science Zhejiang University Hangzhou, 310027, P.R.China zhaoming1999@zju.edu.cn Stan Z. Li Microsoft
More informationTopographic Maps Practice Questions and Answers Revised October 2007
Topographic Maps Practice Questions and Answers Revised October 2007 1. In the illustration shown below what navigational features are represented by A, B, and C? Note that A is a critical city in defining
More informationUsing simulation to calculate the NPV of a project
Using simulation to calculate the NPV of a project Marius Holtan Onward Inc. 5/31/2002 Monte Carlo simulation is fast becoming the technology of choice for evaluating and analyzing assets, be it pure financial
More informationSupporting Information. Fast and Efficient Fragment-Based Lead Generation. by Fully Automated Processing and Analysis of
Supporting Information Fast and Efficient Fragment-Based Lead Generation by Fully Automated Processing and Analysis of Ligand-Observed NMR Binding Data Chen Peng, Alexandra Frommlet, Manuel Perez, Carlos
More informationWhy do we need to improve co-locations of space geodetic techniques?
Why do we need to improve co-locations of space geodetic techniques? Zuheir Altamimi & Xavier Collilieux IGN France 1 Outline ITRF Heritage Current status of technique networks & co-locations Results from
More informationValuing equity-based payments
E Valuing equity-based payments Executive remuneration packages generally comprise many components. While it is relatively easy to identify how much will be paid in a base salary a fixed dollar amount
More informationNI USB-5681 RF Power Meter Specifications
NI USB-568 RF Power Meter Specifications General This document lists specifications for the NI USB-568 RF power meter. Minimum or maximum specifications are warranted under the following conditions: hour
More informationLAB 2 SUPPLEMENT: THE JULES VERNE VOYAGER, JR.
LAB 2 SUPPLEMENT: THE JULES VERNE VOYAGER, JR. For this section of the lab we will be using a very cool web-based map tool that provides access to an amazing suite of state-of-the-art scientific observations
More informationAppendix 1: Time series analysis of peak-rate years and synchrony testing.
Appendix 1: Time series analysis of peak-rate years and synchrony testing. Overview The raw data are accessible at Figshare ( Time series of global resources, DOI 10.6084/m9.figshare.929619), sources are
More informationErrata and updates for ASM Exam C/Exam 4 Manual (Sixteenth Edition) sorted by page
Errata for ASM Exam C/4 Study Manual (Sixteenth Edition) Sorted by Page 1 Errata and updates for ASM Exam C/Exam 4 Manual (Sixteenth Edition) sorted by page Practice exam 1:9, 1:22, 1:29, 9:5, and 10:8
More informationAutodesk Fusion 360: Assemblies. Overview
Overview In this module you will learn how different components can be put together to create an assembly. We will use several tools in Fusion 360 to make sure that these assemblies are constrained appropriately
More informationLeast-Squares Intersection of Lines
Least-Squares Intersection of Lines Johannes Traa - UIUC 2013 This write-up derives the least-squares solution for the intersection of lines. In the general case, a set of lines will not intersect at a
More informationIntroduction to the TI-Nspire CX
Introduction to the TI-Nspire CX Activity Overview: In this activity, you will become familiar with the layout of the TI-Nspire CX. Step 1: Locate the Touchpad. The Touchpad is used to navigate the cursor
More informationMARS STUDENT IMAGING PROJECT
MARS STUDENT IMAGING PROJECT Data Analysis Practice Guide Mars Education Program Arizona State University Data Analysis Practice Guide This set of activities is designed to help you organize data you collect
More informationEstimation of Fractal Dimension: Numerical Experiments and Software
Institute of Biomathematics and Biometry Helmholtz Center Münhen (IBB HMGU) Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch of Russian Academy of Sciences, Novosibirsk
More informationThe Dummy s Guide to Data Analysis Using SPSS
The Dummy s Guide to Data Analysis Using SPSS Mathematics 57 Scripps College Amy Gamble April, 2001 Amy Gamble 4/30/01 All Rights Rerserved TABLE OF CONTENTS PAGE Helpful Hints for All Tests...1 Tests
More informationBlind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections
Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections Maximilian Hung, Bohyun B. Kim, Xiling Zhang August 17, 2013 Abstract While current systems already provide
More informationInternational Journal of Innovative Research in Science, Engineering and Technology Vol. 2, Issue 5, May 2013
ISSN: 2319-8753 International Journal of Innovative Research in Science, Engineering and Technology Vol. 2, Issue 5, May 2013 of vibration are 0.14 rad/s and 0.42 rad/s respectively. The dynamic response
More informationNew constraints on the active tectonic deformation of the Aegean
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi:10.1029/2003jb002830, 2004 New constraints on the active tectonic deformation of the Aegean Marleen Nyst 1 and Wayne Thatcher U.S. Geological Survey, Menlo
More informationStellarium a valuable resource for teaching astronomy in the classroom and beyond
Stellarium 1 Stellarium a valuable resource for teaching astronomy in the classroom and beyond Stephen Hughes Department of Physical and Chemical Sciences, Queensland University of Technology, Gardens
More informationA spreadsheet Approach to Business Quantitative Methods
A spreadsheet Approach to Business Quantitative Methods by John Flaherty Ric Lombardo Paul Morgan Basil desilva David Wilson with contributions by: William McCluskey Richard Borst Lloyd Williams Hugh Williams
More informationMeasurement with Ratios
Grade 6 Mathematics, Quarter 2, Unit 2.1 Measurement with Ratios Overview Number of instructional days: 15 (1 day = 45 minutes) Content to be learned Use ratio reasoning to solve real-world and mathematical
More informationSVPRIBOR ALFA DSL. Users Manual
SVPRIBOR ALFA DSL Users Manual TABLE OF CONTENTS THE GENERAL INORMATION... 3 EXPLOITATION CONDITIONS... 3 CHARACTERISTICS... 3 PACKING LIST... 4 SOKCETS PANNEL... 5 CONTROL... 6 TURNING ON THE DEVICE...
More informationGPS Receiver Test. Conducted by the Department of Mathematical Geodesy and Positioning Delft University of Technology
GPS Receiver Test Conducted by the Department of Mathematical Geodesy and Positioning Delft University of Technology A. Amiri-Simkooei R. Kremers C. Tiberius May 24 Preface For the purpose of a receiver
More informationFace detection is a process of localizing and extracting the face region from the
Chapter 4 FACE NORMALIZATION 4.1 INTRODUCTION Face detection is a process of localizing and extracting the face region from the background. The detected face varies in rotation, brightness, size, etc.
More informationTransient Stability Analysis with PowerWorld Simulator
Transient Stability Analysis with PowerWorld Simulator T6: Storage of Transient Stability Results 2001 South First Street Champaign, Illinois 61820 +1 (217) 384.6330 support@powerworld.com http://www.powerworld.com
More informationHow To Run Statistical Tests in Excel
How To Run Statistical Tests in Excel Microsoft Excel is your best tool for storing and manipulating data, calculating basic descriptive statistics such as means and standard deviations, and conducting
More informationPhase coherency of CDMA caller location processing based on TCXO frequency reference with intermittent GPS correction
Phase coherency of CDMA caller location processing based on TCXO frequency reference with intermittent GPS correction Dingchen Lu, Alfredo Lopez, Surendran K. Shanmugam, John Nielsen and Gerard Lachapelle
More informationUsing Handheld GPS Units in the Field Overview
Using Handheld GPS Units in the Field Overview Most recently revised 3/13/12 Equipment to have with you in the field PC laptop loaded with: - ESRI ArcMap 10 (get from Tufts GIS Support) - DNR Garmin (freeware
More informationThe Viscosity of Fluids
Experiment #11 The Viscosity of Fluids References: 1. Your first year physics textbook. 2. D. Tabor, Gases, Liquids and Solids: and Other States of Matter (Cambridge Press, 1991). 3. J.R. Van Wazer et
More informationExperiment #1, Analyze Data using Excel, Calculator and Graphs.
Physics 182 - Fall 2014 - Experiment #1 1 Experiment #1, Analyze Data using Excel, Calculator and Graphs. 1 Purpose (5 Points, Including Title. Points apply to your lab report.) Before we start measuring
More informationIdentification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting
Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting Murphy Choy Michelle L.F. Cheong School of Information Systems, Singapore Management University, 80, Stamford
More information