Modeling and Inversion of Ocean Acoustic Data for Climate Change
This masters projects is available from the intake of autumn 2023. Please contact the listed supervisors for more information.
An important aspect in understanding climate change is the reliable estimation of the temperature distribution in the ocean. Since temperature is a function of acoustic propagation velocity, acoustic measurements can be very helpful in determining ocean temperatures and thus aid in the understanding of oceanic processes. These acoustics measurements are done in a number of different ways, on a variety of scales (from tens of meters to thousands of kilometers) and using a wide range of frequencies. Accurate processing of these measurements to determine ocean structure, possibly as a function of time, is important but also quite challenging and, in particular, requires a number of different processing and modeling methods.
Hypothesis (scientific problem):
In this master project various processing and modeling and techniques developed in solid Earth physics (including seismic exploration and earthquake seismology) will be used and applied to ocean acoustic data. In particular, various ray-based modeling forward modeling and inversion methods can be used to analyze ocean acoustic data. For example, seismic exploration data can be used to determine and analyze ocean structure, including turbulent mixing, on a scale of 10m to tens of kilometers. Longer range acoustic data can be used to determine velocity variations on a larger scale (hundreds to thousands of kilometers). It should be noted that various processing, modeling and inversion tools already exist, and to some extent have been applied to ocean acoustics. However, a lot of work still remains to be done.
Further development of the available processing and modeling, techniques, requires advances in terms of both theory and numerical methods. For example, when using ray based modeling techniques over longer distances it is important to efficiently deal with multipathing. So far not much attention has been paid to this problem. Moreover, the frequency dependence of the acoustic signals contains a wealth of information on the structure of the ocean over a wide range of scales. The sensitivity of the acoustic signal to these structures is not well understood. Another, but related, processing method is provided by physics based machine learning algorithms. Thus a variety of approaches can be used to analyze ocean acoustic data with the goal of obtaining a better understanding of ocean structure and dynamics and therefore the climate system. In this project the particular application and method very much depends on the interest of the student.