Designing the configuration of the Geodetic-Geodynamic Network in Israel Gilad Even-Tzur Department of Mapping and Geo-Information Engineering Faculty.

Slides:



Advertisements
Similar presentations
Dept of Bioenvironmental Systems Engineering National Taiwan University Lab for Remote Sensing Hydrology and Spatial Modeling STATISTICS Hypotheses Test.
Advertisements

Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 9 Inferences Based on Two Samples.
Anthony Greene1 Simple Hypothesis Testing Detecting Statistical Differences In The Simplest Case:  and  are both known I The Logic of Hypothesis Testing:
A Kinematic Fault Network Model for Crustal Deformation (including seismicity of optimal locking depth, shallow surface creep and geological constraints)
AP Statistics Section 11.2 B. A 95% confidence interval captures the true value of in 95% of all samples. If we are 95% confident that the true lies in.
Statistical Decision Making
Employment of a Permanent Monitoring GPS Network at the Seismic Area of Volvi, Greece P. D. Savvaidis, I. M. Ifadis Aristotle University of Thessaloniki,
Confidence Intervals © Scott Evans, Ph.D..
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering On-line Alert Systems for Production Plants A Conflict Based Approach.
1 Hypothesis Testing In this section I want to review a few things and then introduce hypothesis testing.
GTECH 201 Session 08 GPS.
Chapter 26: Comparing Counts. To analyze categorical data, we construct two-way tables and examine the counts of percents of the explanatory and response.
Chi-Square Test.
Permanent GPS Stations in Israel as Basis for
Why North China is seismically active while South China remains largely aseismic? Youqing Yang & Mian Liu, Dept. of geol. University of Missouri-Columbia.
AM Recitation 2/10/11.
Permanent GPS Stations and their Influence on the Geodetic Surveys in Israel Gershon Steinberg Survey of Israel 1 Lincoln St. Tel-Aviv 65220, Israel Gilad.
STATISTICS HYPOTHESES TEST (I) Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University.
Statistical inference: confidence intervals and hypothesis testing.
GEODETIC INSTITUTE LEIBNIZ UNIVERSITY OF HANNOVER GERMANY Ingo Neumann and Hansjörg Kutterer The probability of type I and type II errors in imprecise.
1 Level of Significance α is a predetermined value by convention usually 0.05 α = 0.05 corresponds to the 95% confidence level We are accepting the risk.
Sample size determination Nick Barrowman, PhD Senior Statistician Clinical Research Unit, CHEO Research Institute March 29, 2010.
13.1 Goodness of Fit Test AP Statistics. Chi-Square Distributions The chi-square distributions are a family of distributions that take on only positive.
Moisture observation by a dense GPS receiver network and its assimilation to JMA Meso ‑ Scale Model Koichi Yoshimoto 1, Yoshihiro Ishikawa 1, Yoshinori.
Significance Toolbox 1) Identify the population of interest (What is the topic of discussion?) and parameter (mean, standard deviation, probability) you.
Chapter 12 Tests of a Single Mean When σ is Unknown.
Chapter 11 Inference for Tables: Chi-Square Procedures 11.1 Target Goal:I can compute expected counts, conditional distributions, and contributions to.
Blue – comp red - ext. blue – comp red - ext blue – comp red - ext.
A comparison of the ability of artificial neural network and polynomial fitting was carried out in order to model the horizontal deformation field. It.
Chi-Square Test.
L Berkley Davis Copyright 2009 MER301: Engineering Reliability Lecture 9 1 MER301:Engineering Reliability LECTURE 9: Chapter 4: Decision Making for a Single.
Jayne Bormann and Bill Hammond sent two velocity fields on a uniform grid constructed from their test exercise using CMM4. Hammond ’ s code.
Least Squares Estimate Additional Notes 1. Introduction The quality of an estimate can be judged using the expected value and the covariance matrix of.
Chapter 8 Single Sample Tests Part II: Introduction to Hypothesis Testing Renee R. Ha, Ph.D. James C. Ha, Ph.D Integrative Statistics for the Social &
Österreichische Akademie der Wissenschaften (ÖAW) / Institut für Weltraumforschung (IWF), 8042 Graz, Austria, Contact:
T tests comparing two means t tests comparing two means.
The Idea of the Statistical Test. A statistical test evaluates the "fit" of a hypothesis to a sample.
In Bayesian theory, a test statistics can be defined by taking the ratio of the Bayes factors for the two hypotheses: The ratio measures the probability.
2002/05/07ACES Workshop Spatio-temporal slip distribution around the Japanese Islands deduced from Geodetic Data Takeshi Sagiya Geographical Survey Institute.
Reprocessing CEGRN campaigns M. Becker(1), A. Caporali(2), R. Drescher(1), L. Gerhatova(5), G. Grenerczy(3), C. Haslinger(4), J. Hefty(5), S.
Lec. 19 – Hypothesis Testing: The Null and Types of Error.
Statistical Inference for the Mean Objectives: (Chapter 8&9, DeCoursey) -To understand the terms variance and standard error of a sample mean, Null Hypothesis,
REC Savannah, Febr. 22, 2006 Title Outlier Detection in Geodetic Applications with respect to Observation Imprecision Ingo Neumann and Hansjörg.
Chi Square Test of Homogeneity. Are the different types of M&M’s distributed the same across the different colors? PlainPeanutPeanut Butter Crispy Brown7447.
Chi-Square Test.
More on Inference.
STATISTICS HYPOTHESES TEST (I)
ESTIMATION.
Significance Test for the Difference of Two Proportions
(5) Notes on the Least Squares Estimate
Permanent GPS Stations and their Influence on the
Chapter 4. Inference about Process Quality
The Chi-Squared Test Learning outcomes
Computer Vision Lecture 4: Color
Hypothesis Tests for a Population Mean in Practice
Chapter 9 Hypothesis Testing.
Section 11.2: Carrying Out Significance Tests
More on Inference.
Chi-Square Test.
Chi-Square Test.
Statistical Inference for Managers
Chi-Square Test.
Statistics Chapter 10 Section 4.
Last Update 12th May 2011 SESSION 41 & 42 Hypothesis Testing.
Chapter 14.1 Goodness of Fit Test.
Inference as Decision Section 10.4.
Shapes.
Statistical Inference for the Mean: t-test
STATISTICS HYPOTHESES TEST (I)
Inference for Distributions of Categorical Data
Presentation transcript:

Designing the configuration of the Geodetic-Geodynamic Network in Israel Gilad Even-Tzur Department of Mapping and Geo-Information Engineering Faculty of Civil and Environmental Engineering TECHNION - Israel Institute of Technology ISGDM-2005, March, Jaén, Spain

The Geodetic-Geodynamic Network (G1) ► Includes 160 points that homogeneously cover the state of Israel (Blue circles and Red squares) ► The location of the points was determined mainly according to geological considerations ► The points were built according to very high technical specifications, to ensure their geotechnical stability

The goal of the Geodetic- Geodynamic Network ► A potential geodetic network for monitoring deformations in primary and secondary known faults ► Serves as the major geodetic control network of Israel

The First Measurement Campaign ► During 1996 the G1 network was measured for the first time ► The network was measured by four GPS receivers ► Sessions of 24 hours ► Most network points were measured in two independent sessions ► The data processing was carried out using the BERNESE

The Network Sensitivity ► Based on the first campaign, a sensitivity analysis was performed in the northern part of the network ► The analysis indicated that the sensitivity of the network was too low ► The analysis also indicated that an identical second measurement campaign will not be sufficient to detect possible movements and deformations A new network design is needed

Network Configuration of the Second Campaign ► The second campaign was held in 2002 ► Only 100 points were measured (Blue circles) ► 5 new points were fixed in the northern part of the network (Yellow circles ) ► 11 continuous permanent GPS stations were operated (Green triangles)

Vector Configuration Design of the Network ► An effective design of the GPS measurements decreases campaign costs and increases the accuracy and reliability of the network ► The goal of the design is to improve the network, so that it would enable the detection and measurements of expected movements and deformations ► A method, based on sensitivity analysis, was used for the GPS vector configuration design ► The method uses a velocity field of the network points, calculated from an assumed geological model

Geological Model ► Locked Fault model in the Dead Sea Rift the fault is locked from the surface down to depth D, and slips freely below this depth by V millimeters per year

Sensitivity Analysis ► Based on statistical test of Hypothesis Accepted if: ► If is true, the test statistic has a non-central F distribution with non-centrality parameter given by :

Sensitivity Analysis ► We define as the boundary value of λ, which will cause the null hypothesis to be rejected at probability levels α and β, the value is an implicit function ► Our aim is designing a monitoring network, which will enable the rejection of the null hypothesis and the acceptance of the alternative hypothesis  Sensitive Network

Sensitivity Analysis ► Since the first GPS campaign has already been carried out in 1996 N 1 has been defined ► We create the normal matrix N 2 which contains the sessions with the most effective contribution to the sensitivity of the network 8 sessions provide a sufficiently sensitive network

Configuration Design of the Network ► To increase the reliability of the network, each point was measured in three independent sessions ► The duration of each session was planned for 8 hours with epoch interval of 30 sec ► The network was designed for measurements using four receivers 92 sessions for 105 points

Broken lines - The eight sessions that provide sufficiently sensitive network The designed sessions of the northern part of the Geodetic-Geodynamic network

The ability of the network to sense horizontal velocity The typical precision of the network points: ► 1996: 3-4 mm for the horizontal component ► 2002: 2-3 mm for the horizontal component We can roughly estimate the ability of the network to sense horizontal velocity along the Dead Sea rift for a velocity of two millimeters per year