Advance Programming In Geophysics

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

None

Course Contents

In this course; detailed information will be given in Matlab programming language, including software of some forward and inverse solution programs in geophysics, solution of differential equations, toolboxes of Matlab software, statistics and program development using Matlab language.

Weekly Schedule

1) 1.1 Introduction 1.2 Data Collection 1.3 Types of Data 1.4 Methods of Data Analysis
2) 2.1 Introduction to MATLAB 2.2 Variables 2.3 Array and Matrix 2.4 Data Structures and Classes of Objects 2.5 Data Storage and Handling 2.6 Control Flow 2.7 Scripts and Functions 2.8 Basic Visualization Tools 2.9 Generating Code to Recreate Graphics 2.10 Publishing M-Files 2.11 Creating Graphical User Interfaces
3) 3.1 Developing some forward modeling programs 3.2 Direct Current Resistivity (DCR) program 3.3 Seismic Refraction Program 3.4 Seismic Reflection Program 3.5 Ground Penetrating Radar (GPR) Program
4) 4.1 Developing some inverse modeling programs 4.2 Direct Current Resistivity (DC) program 4.3 Optimization of GPR Method
5) 5.1 Some geophysical software developed with Matlab 5.2 CREWES 5.3 Some data proccesing programs (seismic data processing)
6) 6.1 Matlab toolboxes 6.2 Wavelet toolbox 6.3 Partial Differential Equation toolbox 6.4 Digital Signal Processing toolbox
7) 7.1 Univariate Statistics 7.2 Empirical Distributions 7.3 Examples of Empirical Distributions 7.4 Theoretical Distributions 7.5 Examples of Theoretical Distributions 7.6 Hypothesis Testing 7.7 The t-Test 7.8 The F-Test 7.9 The ?2-Test
8) Midterm
9) 9.1 : Bivariate Statistics 9.2 Correlation Coefficients 9.3 Classical Linear Regression Analysis 9.4 Analyzing the Residuals 9.5 Bootstrap Estimates of the Regression Coefficients 9.6 Jackknife Estimates of the Regression Coefficients 9.7 Cross Validation 9.8 Reduced Major Axis Regression 9.9 Curvilinear Regression 9.10 Nonlinear and Weighted Regression
10) 10.1 Time-Series Analysis 10.2 Generating Signals 10.3 Auto-Spectral and Cross-Spectral Analysis 10.4 Examples of Auto-Spectral and Cross-Spectral Analysis 10.5 Interpolating and Analyzing Unevenly-Spaced Data 10.6 Evolutionary Power Spectrum 10.7 Lomb-Scargle Power Spectrum 10.8 Wavelet Power Spectrum 10.9 Detecting Abrupt Transitions in Time Series 10.10 Nonlinear Time-Series Analysis
11) 11.1 Signal Processing 11.2 Generating Signals 11.3 Linear Time-Invariant Systems 11.4 Convolution, Deconvolution and Filtering 11.5 Comparing Functions for Filtering Data Series 11.6 Recursive and Nonrecursive Filters 11.7 Impulse Response 11.8 Frequency Response 11.9 Filter Design 11.10 Adaptive Filtering
12) 12.1 Spatial Data 12.2 The Global Geography Database GSHHG 12.3 The 1-Minute Gridded Global Relief Data ETOPO1 12.4 The 30-Arc Seconds Elevation Model GTOPO30 12.5 The Shuttle Radar Topography Mission SRTM 12.6 Exporting 3D Graphics to Create Interactive Documents 12.7 Gridding and Contouring 12.8 Comparison of Methods and Potential Artifacts 12.9 Statistics of Point Distributions 12.10 Analysis of Digital Elevation Models 12.11 Geostatistics and Kriging
13) 13.1 Image Processing 13.2 Data Storage 13.3 Importing, Processing and Exporting Images 13.4 Importing, Processing and Exporting LANDSAT Images 13.5 Importing and Georeferencing TERRA ASTER Images 13.6 Processing and Exporting EO-1 Hyperion Images 13.7 Digitizing from the Screen 13.8 Image Enhancement, Correction and Rectification 13.9 Color-Intensity Transects Across Varved Sediments 13.10 Grain Size Analysis from Microscope Images 13.11 Quantifying Charcoal in Microscope Images 13.12 Shape-Based Object Detection in Images
14) 14.1 Multivariate Statistics 14.2 Principal Component Analysis 14.3 Independent Component Analysis 14.4 Discriminant Analysis 14.5 Cluster Analysis 14.6 Multiple Linear Regression
15) 15.1 Directional Data 15.2 Graphical Representation 15.3 Empirical Distributions 15.4 Theoretical Distributions 15.5 Test for Randomness of Directional Data 15.6 Test for the Significance of a Mean Direction 15.7 Test for the Difference between Two Sets of Directions
16) Final Exam

Recommended or Required Reading

1- Aster,R.C. ,Borchers, B. ,Thurber, C.H. , 2013, Parameter Estimation and Inverse Problems, Elsevier.
2- Trauth, M.H. , 2014, MATLAB Recipes for Earth Sciences, Springer.
3- Altıntaş, A. , 2006, Matematiksel Bir Yöntem Olarak Matlab ve Genel Uygulamaları, Değişim Yayınları.
4- Dal, D. , 2011, Matlab ile Programlama, Ekin Yayınevi.
5- Mousa, W. A., Al-Shuhail A. A., 2011, Processing of Seismic Reflection Data Using MATLAB, Morgan and Claypool Publishers.
6- Margrave, G.F., 2003, Numerical Methods of Exploration Seismology with Algorithms in MATLAB, The University of Calgary.

Planned Learning Activities and Teaching Methods

1) Lecture
2) Drill and Practice
3) Modelling
4) Simulation
5) Self Study
6) Problem Solving
7) Project Based Learning

Assessment Methods and Criteria

Contribution of Semester Studies to Course Grade	70%
Number Percentage Semester Studies Midterm Examination 1 30% Quiz 4 50% Project 1 20%
Contribution of Final Examination to Course Grade	30%
Total	100%

Language of Instruction

Turkish

Work Placement(s)

Not Required