Short Courses

• Short courses/preconference workshops will be organized before the conference.
• Short courses will start at 10.00 hrs and will be held at the Conference Venue, Jawaharlal Nehru University. Participants are expected to bring their own laptop.
• Short courses registration period is between 1st April 2014 and 31st July 2014.
Downloadable short courses registration form will be made available here.
Short courses pre-registrations forms should be sent to iamg14@yahoo.com
.
• The minimum number of participants (at least 10) must be pre-registered to hold the course. If the minimum number of pre-registrations will be achieved participants will receive a confirmation e-mail. After confirmation mail each participant should pay the registration fee on or before 15th August 2014.

Course 1: AN INTRODUCTION TO COMPOSITIONAL DATA ANALYSIS

Prof. Dr. Pawlowsky-Glahn, Vera (University of Girona, Spain) and Prof. Dr. Egozcue, Juan José (Technical University of Catalonia, Spain)
Course Fee: $250 (foreign delegates) and Rs. 10,000/- (Indian delegates)

The course will cover the basic principles and methods of compositional data analysis, and introduce those attending to analysing data with CoDaPack and to the interpretation of obtained results. The course will combine theoretical lectures with hands-on practical analysis of data with CoDaPack.

15.10.2014 • Presentation. Principles of CoDa analysis.
• The Aitchison geometry of the simplex.
• Coordinates for CoDa. The principle of working on coordinates.
Lab: Introduction to CoDaPack. Ternary-quaternary diagrams, perturbation,
centering, scaling. Spurious correlation.
16.10.2014 • Basic statistical tools.
• Exploratory analysis, variation array.
• Principal components.
• Biplot and CoDa-dendrogram.
• Processes in the simplex and mixture.
• Growth-decay processes and linear regression on the simplex.

Course 2: TIME SERIES ANALYSIS IN GEOLOGY: AN ANTIQUATED METHOD?

PD Dr. Gossel, Wolfgang (University of Halle, Germany)
Course Fee: $125 (foreign delegates) and Rs. 5,000/- (Indian delegates)

How can time series analysis contribute to general and applied geological data exploration? Is this old fashioned method useless or do new developments support geoscientists in process analysis and perhaps in prediction? Multiple data sets and standard software as well as software written by the course lecturer will help to understand and apply the methods.

16.10.2014 • Steps and principles of time series analysis.
• Classical time series analysis: Trends, periods and autocorrelation.
• New developments: Time series analysis in high resolution, analysis of unevenly spaced data sets and spatial application.
Labs: Introduction to diverse OpenSource tools.
Analysis of diverse data sets, mainly applied geosciences.

Course 3: A PRIMER ON GEOSTATISTICS

Prof. Dr. Bhabesh C. Sarkar (Indian School of Mines, India)
Course Fee: $250 (foreign delegates) and Rs. 10,000/- (Indian delegates)

The course is designed for geoscientists and engineers who are involved in mineral/petroleum resources modelling and characterization. The programme would illustrate why and how geostatistics may be used in mineral/petroleum exploration, resource estimation, sample optimization and other related problems of natural resources.

15.10.2014 • Basic concepts of exploration statistics; Normal (Gaussian) and Lognormal distribution modeling
• Concepts of Geostatistics, Theory of regionalized variables
• Spatial data analysis and semi-variography
16.10.2014 • Extension and estimation variances
• Kriging and optimal evaluation
• Geostatistical mineral inventory estimation; Grade-tonnage relationships
• Geostatistical applications in exploration and mining

Course 4: Mathematical Morphology in Geosciences and Geoinformatics

Prof. Dr. B. S. Daya Sagar (Systems Science and Informatics Unit, Indian Statistical Institute-Bangalore Centre, India)
Course Fee: $250 (foreign delegates) and Rs.10,000 (Indian delegates)

Processing of remotely sensed data in both spatial and frequency domains has received wide attention. The application of remote sensing in various fields is greatly realized in the last three decades. One of the data derivable from remotely sensed data is a Digital Elevation Model (DEM) that provides rich clues about physiographic constitution of Earth planet, and Earth-like planetary surfaces. Remotely sensed data are available for various phenomena related to terrestrial, lunar, planetary surfaces, and atmospheric phenomena such as clouds in spatiotemporal mode. To address the intertwined topics—like pattern retrieval, pattern analysis, spatial reasoning, and simulation and modeling for understanding spatiotemporal behaviors of several of terrestrial phenomena and processes that could be acquired through remote sensing mechanisms—various original algorithms and modeling techniques that are mainly based on mathematical morphology have been developed and demonstrated their utility. This course presents applications of mathematical morphology and scaling theories in addressing those mentioned intertwined topics.

15.10.2014
• Introduction to Mathematical Morphology
• Mathematical Morphology in Terrestrial Pattern Retrieval
• Mathematical Morphology in Terrestrial Pattern Analysis
Part 1: Terrestrial surface characterization: a quantitative perspective
Part 2: Size distributions, spatial heterogeneity and scaling laws
Part 3: Morphological shape decomposition: scale invariant but shape dependent measures
Part 4: Granulometries, convexity measures and geodesic spectrum for DEM Analyses
•Mathematical Morphology in Geomorphologic Modelling and Simulation
Part 1: Fractal-Skeletal-Based Channel Network Model
Part 2: Synthetic models to understand spatio-temporal dynamics of certain geo(morpho)logical processes
16.10.2014
• Mathematical Morphology in Quantitative Spatial Reasoning and Visualization
• Mathematical Morphology in Spatial Interpolations
Part 1: Conversion of point-data into polygonal map via WSKIZ
Part 2: Visualization of spatiotemporal behavior of discrete maps via generation of recursive median elements
• Directional Granulometries in Shape Classification
• Morphological Distances in Classification of Zones, Pairs of Zones, and Clusters in a Spatial System
• Quantitative Characterization of Complex Porous Phase via Mathematical Morphology and Fractal Geometry