Catalog Description:

• EOC 3631: Ocean and Environmental Data Analysis.
  Methods of time series data analysis are presented, including probability and statistics, power spectral density, correlation, sampling and coherence. Environmental data time series analysis include ambient acoustic noise, temperature, salinity, waves and bathymetry with applications using MATLAB.

Pre-requisites:

• MAP4306 (Engineering Math II) and EOC3130 (Ocean Engineering Laboratory).

Textbook:

• Probabilistic Methods of Signal and System Analysis, by Cooper and McGillem, Oxford Press.

Other References:

• Lecture Notes (attendance required).
• Matlab Handbook, the MathWorks, Inc.

Coordinator:

• Pr. Pierre-Philippe J. Beaujean

Goals/Objectives:

• The objective of the course is to provide the students with a basic and applied knowledge of probabilistic and statistical methods to analyze random phenomena, with an emphasis on ocean environmental data study.

Course Topics:

1. Introduction to probability.
2. Random variables fundamentals.
3. Extension to multiple random variables.
4. Elements of statistics.
5. Classification of random processes.
6. Time and space correlation functions.
7. Spectral density function, Fourier analysis.
8. Applications: wave spectral analysis, bathymetric survey.

Computer Projects:

1. Computation of histogram, probability density and distribution functions, moments and quadratic error for ocean data.
2. Linear regression, linear correlation factor and confidence intervals.
3. Time and spatial correlation estimate, periodigram and spectral analyzer.

Laboratory Projects:

• Lab. 1: Random properties of ocean waves.
• Lab. 2: Determination of ocean temperature, salinity and sound speed depth profile.
• Lab. 3: Spectral analysis of ocean ambient noise

Grading Policy:

• Homework -- 5%
• Oral Examination -- 10%
• Laboratory reports -- 15%
• Exam I -- 20%
• Exam II -- 20%
• Final exam -- 30%

Course Outcomes:

• An ability to study ocean phenomena as random events and understand the concept of estimation and accuracy.
• A thorough understanding of time and frequency analysis of random phenomena, with an emphasis on ocean physics.
• An ability to associate a confidence level to any numerical estimate, from probability density function and time coherence to power spectral density (Fourier) analysis.
• An ability to measure the correlation between two physical phenomena, such as ocean ambient noise and surface wave activity for example.
• An ability to make measurements of ambient acoustic noise, surface waves and sound velocity profiles, followed by a thorough data analysis.